Comparison of boosting algorithms in a complex multiclass problem - Spotify Song Popularity in multiple classes

CUNEF - MS Data Science for Finance

Machine Learning

Start Date: 17 January 2019

End Date: 31 January 2019

Authors: Jordi Tarroch and José María Álvarez

TABLE OF CONTENTS

Executive Summary

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Studying spotify track data we are looking for the main features and extract the most valuable information of it, in order to understand which features make a song popular. We'll do that by:

  • Firstly preprocessing the data.
  • Fitting some models with this data.
  • Selecting the best model for our study case.

Objectives:

The main objective of this project is to find the best model to classify a song by popularity (in ten popularity levels) given the characteristics of the song in order to understand what make a song popular and predict how popular a song can be. To do this, a supervised classification model will be estimated using the data from Lending Club Loan Data. The type of models to be compared are Logistic Regression, Support Vector Machine, Random Forest and XGBOOST. Additionaly, two new models will be tested: NGBoost and CatBoost.

Question:

  • Is there a model that predicts multiclass popularity levels better than the rest?
  • If so, which features are the most important for a song's popularity?

KPI:

The best model will be the one that outperforms the rest in the following scores:

- Accuracy: $$ \text { Accuracy }=\frac{t p + t n}{t p+f p+t n+f n } $$

- Precision: also called positive predictive value, is the fraction of relevant instances among the retrieved instances. $$ \text { Precision }=\frac{t p}{t p+f p} $$ - Recall: also known as sensitivity, is the fraction of the total amount of relevant instances that were actually retrieved. $$ \text { Recall }=\frac{t p}{t p+f n} $$

- F1-score: In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test's accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the number of all relevant samples (all samples that should have been identified as positive). The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.

$$ F_{1}=\left(\frac{2}{\mathrm{recall}^{-1}+\text { precision }^{-1}}\right)=2 \cdot \frac{\text { p } \cdot \text { r }}{\text { p }+\text { r }} $$

Results

Based on the proposed scores, the best model to predict the test set was the CatBoost Model; NGBoost had the best precision, but in accuracy, recall and f1-score, the best model is CatBoost. Because the classification is not dichotomous but multiclass, there is no graphical representation of the ROC curve due to dimensionality; In addition, it is not so easy to relate prediction metrics (for example, accuracy) to an established benchmark, the best benchmark for comparison is that it has 100% successes in some class and zero in the others (which on average equals a distribution uniform data by class) which in this case corresponds to 10% (due to the ten classes) [16.6% for 6 classes]

Accuracy precision recall f1-score
Logistic Regression 0.36 0.31 0.36 0.27
SVM 0.35 0.22 0.35 0.25
SVM Polynomial 0.37 0.42 0.37 0.29
SVM RBF 0.37 0.41 0.37 0.29
SVM Sigmoid 0.26 0.27 0.26 0.26
Random Forest 0.35 0.34 0.35 0.3
XGBoost 0.37 0.41 0.37 0.29
CatBoost 0.38 0.4 0.38 0.31
NGBoost 0.37 0.43 0.37 0.28

Looking at the feature importance for the models, every model had different features as the main characteristics to decide the classification, but some interesting conclusions arose from this initial approach.

Feature Importance Logistic Regression SVM Random Forest XGBoost CatBoost NGBoost
1 danceability loudness duration_ms duration_ms duration_ms duration_ms
2 instrumentalness valence loudness loudness danceability loudness
3 explicit time_signature danceability danceability loudness danceability
4 energy explicit valence valence energy valence
5 valence acousticness acousticness acousticness speechiness acousticness
6 mode duration_ms speechiness speechiness valence speechiness
7 liveness key tempo tempo liveness tempo
8 acousticness instrumentalness energy energy acousticness energy
9 speechiness danceability liveness liveness instrumentalness liveness
10 duration_ms mode instrumentalness instrumentalness tempo instrumentalness
11 time_signature tempo key key key key
12 tempo speechiness mode mode explicit mode
13 loudness energy time_signature time_signature mode time_signature
14 key liveness explicit explicit time_signature explicit

XGBOOST and Random Forest models selected a group of features as the most important; duration_ms, loudness, danceability, valence, acousticness, among other. Every feature for these models is related somehow to the rest of the features; happening in both models. On the other hand, the SVM Linear Kernel and the Logistic Regression (both linear) selected a group of features as the most important as well; explicit is the main feature diference between the two groups of models. The only variable shared by the groups is danceability. Apparently, there are two approaches for this classification problem: a linear approach and a non-linear; only by looking at the features each model selects.

Therefore, following what the best model says, if you want a song to reach the maximum level of popularity you must take into account the duration of the song; how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity; the energy it produces, how fast, loud and noisy it is; and level of decibels.

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LIBRARIES

In [1]:
#spotify api
import spotipy
#dataframes
import pandas as pd

#scaling
from sklearn.preprocessing import MinMaxScaler

from spotipy.oauth2 import SpotifyClientCredentials

#Measure time
import time

#lasso
from sklearn import linear_model
from sklearn import datasets
from sklearn.svm import l1_min_c

#maths
import numpy as np

#visualization
import seaborn as sns
import graphviz
import panel
from matplotlib import pyplot as plt

#seed
import random

#Grid search
from sklearn.model_selection import GridSearchCV 
from sklearn.svm import SVC 

# logisticRegression
from sklearn.linear_model import LogisticRegression
import statsmodels.api as sm

#svm
from sklearn import svm
from sklearn import datasets, metrics, model_selection, svm

#Random Forest
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import export_graphviz
from subprocess import call

# Display in jupyter notebookfrom IPython.display import ImageImage(filename = 'tree.png')
import os
import six
from sklearn import tree


#xgboost
import xgboost as xgb
from xgboost import XGBClassifier

# catboost and ngboost
import catboost
import ngboost
from catboost import Pool, CatBoostClassifier

#performance metrics
from sklearn.metrics import classification_report, confusion_matrix, make_scorer, precision_score, recall_score, accuracy_score
# confusion matrix
from sklearn import metrics

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Data Set

Spotify API

Based on simple REST principles, the Spotify Web API endpoints return JSON metadata about music artists, albums, and tracks, directly from the Spotify Data Catalogue. Web API also provides access to user related data, like playlists and music that the user saves in the Your Music library. Such access is enabled through selective authorization, by the user. The data obtain through this API is limited and the databases may only vary in number and year when data is not from a users playlist.

Spotipy

Spotipy is a lightweight Python library for the Spotify Web API. With Spotipy you get full access to all of the music data provided by the Spotify platform. Spotipy supports all of the features of the Spotify Web API including access to all end points, and support for user authorization.

spotipy supports two authorization flows:

  • The Authorization Code flow This method is suitable for long-running applications which the user logs into once. It provides an access token that can be refreshed.
  • The Client Credentials flow The method makes it possible to authenticate your requests to the Spotify Web API and to obtain a higher rate limit than you would with the Authorization Code flow.
In [2]:
# CONNECT TO SPOTIFY


cid ="b43c7befbf6a4de29308d2dd70056f20" #get these from spotify for developers website
secret = "518b76d242ba4a07b37b735ec6ab1393"

client_credentials_manager = SpotifyClientCredentials(client_id=cid, client_secret=secret)
sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)

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Data Set from Playlist

In [3]:
def get_playlist_tracks(username,playlist_id):
    results = sp.user_playlist_tracks(username,playlist_id)
    tracks = results['items']
    while results['next']:
        results = sp.next(results)
        tracks.extend(results['items'])
    return tracks
In [4]:
#GET SONG METRICS FROM PLAYLIST

userid = 'gjordj'
playlistURI = "1z6vJLmcITe0emKZQecTJC"
#connect to playlist
# playlist = sp.user_playlist(userid, playlistURI)
playlist = get_playlist_tracks(userid, playlistURI)
In [5]:
feature_list = []
reduce_sample = 2
#obtain data
for i in range(round(len(playlist)/reduce_sample)): 
    feature_list.append(sp.audio_features(playlist[i]['track']['id'])[0])

retrying ...1secs
retrying ...1secs
In [6]:
dfmet = pd.DataFrame(feature_list)
dfmet.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1171 entries, 0 to 1170
Data columns (total 18 columns):
acousticness        1171 non-null float64
analysis_url        1171 non-null object
danceability        1171 non-null float64
duration_ms         1171 non-null int64
energy              1171 non-null float64
id                  1171 non-null object
instrumentalness    1171 non-null float64
key                 1171 non-null int64
liveness            1171 non-null float64
loudness            1171 non-null float64
mode                1171 non-null int64
speechiness         1171 non-null float64
tempo               1171 non-null float64
time_signature      1171 non-null int64
track_href          1171 non-null object
type                1171 non-null object
uri                 1171 non-null object
valence             1171 non-null float64
dtypes: float64(9), int64(4), object(5)
memory usage: 164.8+ KB
In [7]:
#SONG METRICS DATA CLEANUP
columns_to_drop_met = ['analysis_url','track_href','type','uri']
dfmet.drop(columns_to_drop_met, axis=1,inplace=True)

dfmet.rename(columns={'id': 'track_id'}, inplace=True)

dfmet.shape
dfmet.info()
dfmet.head()

#Song metrics collection is done here

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1171 entries, 0 to 1170
Data columns (total 14 columns):
acousticness        1171 non-null float64
danceability        1171 non-null float64
duration_ms         1171 non-null int64
energy              1171 non-null float64
track_id            1171 non-null object
instrumentalness    1171 non-null float64
key                 1171 non-null int64
liveness            1171 non-null float64
loudness            1171 non-null float64
mode                1171 non-null int64
speechiness         1171 non-null float64
tempo               1171 non-null float64
time_signature      1171 non-null int64
valence             1171 non-null float64
dtypes: float64(9), int64(4), object(1)
memory usage: 128.2+ KB
Out[7]:
acousticness danceability duration_ms energy track_id instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 0.0180 0.584 228173 0.752 0zCfmceT1YvtZXGbR6Roht 0.000004 8 0.647 -6.689 1 0.0580 125.797 4 0.791
1 0.0834 0.543 192093 0.848 3gGHAScxUDsJqcAM6ZvjuH 0.000000 10 0.799 -6.058 0 0.0531 102.009 4 0.290
2 0.0340 0.818 225983 0.803 6b8Be6ljOzmkOmFslEb23P 0.000000 1 0.153 -4.282 1 0.0797 106.970 4 0.632
3 0.0736 0.608 185352 0.798 6DNtNfH8hXkqOX1sjqmI7p 0.000000 6 0.156 -5.092 0 0.0432 92.943 4 0.501
4 0.1010 0.707 254739 0.594 1dzk5fQIYJ3MMmgoRVw2qw 0.002140 5 0.122 -7.461 1 0.0470 88.000 4 0.193
In [8]:
#GET SONG NAME FROM PLAYLIST


# create empty lists where the results are going to be stored
artist_name = []
track_name = []
popularity = []
track_id = []
explicit = []

#Get playlist
track_results = get_playlist_tracks(userid, playlistURI)
# track_results = sp.user_playlist(userid, playlistURI)["tracks"]["items"] #edit this to put in your spotify playlist URI code

track_results
Out[8]:
[{'added_at': '2017-12-05T13:44:23Z',
  'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
   'href': 'https://api.spotify.com/v1/users/gjordj',
   'id': 'gjordj',
   'type': 'user',
   'uri': 'spotify:user:gjordj'},
  'is_local': False,
  'primary_color': None,
  'track': {'album': {'album_type': 'compilation',
    'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0LyfQWJT6nXafLPZqxe9Of'},
      'href': 'https://api.spotify.com/v1/artists/0LyfQWJT6nXafLPZqxe9Of',
      'id': '0LyfQWJT6nXafLPZqxe9Of',
      'name': 'Various Artists',
      'type': 'artist',
      'uri': 'spotify:artist:0LyfQWJT6nXafLPZqxe9Of'}],
    'available_markets': [],
    'external_urls': {'spotify': 'https://open.spotify.com/album/5p0H50uFCdWTpLY640HoPc'},
    'href': 'https://api.spotify.com/v1/albums/5p0H50uFCdWTpLY640HoPc',
    'id': '5p0H50uFCdWTpLY640HoPc',
    'images': [{'height': 640,
      'url': 'https://i.scdn.co/image/ab67616d0000b2732a307d64201d08144f68cc49',
      'width': 640},
     {'height': 300,
      'url': 'https://i.scdn.co/image/ab67616d00001e022a307d64201d08144f68cc49',
      'width': 300},
     {'height': 64,
      'url': 'https://i.scdn.co/image/ab67616d000048512a307d64201d08144f68cc49',
      'width': 64}],
    'name': 'La La Land (Original Motion Picture Soundtrack)',
    'release_date': '2016-12-09',
    'release_date_precision': 'day',
    'total_tracks': 15,
    'type': 'album',
    'uri': 'spotify:album:5p0H50uFCdWTpLY640HoPc'},
   'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/6T70tWxqyIpdVbsFvPzqM3'},
     'href': 'https://api.spotify.com/v1/artists/6T70tWxqyIpdVbsFvPzqM3',
     'id': '6T70tWxqyIpdVbsFvPzqM3',
     'name': 'La La Land Cast',
     'type': 'artist',
     'uri': 'spotify:artist:6T70tWxqyIpdVbsFvPzqM3'}],
   'available_markets': [],
   'disc_number': 1,
   'duration_ms': 228173,
   'episode': False,
   'explicit': False,
   'external_ids': {'isrc': 'USUG11600653'},
   'external_urls': {'spotify': 'https://open.spotify.com/track/0zCfmceT1YvtZXGbR6Roht'},
   'href': 'https://api.spotify.com/v1/tracks/0zCfmceT1YvtZXGbR6Roht',
   'id': '0zCfmceT1YvtZXGbR6Roht',
   'is_local': False,
   'name': 'Another Day Of Sun - From "La La Land" Soundtrack',
   'popularity': 8,
   'preview_url': None,
   'track': True,
   'track_number': 1,
   'type': 'track',
   'uri': 'spotify:track:0zCfmceT1YvtZXGbR6Roht'},
  'video_thumbnail': {'url': None}},
 {'added_at': '2017-12-05T13:44:23Z',
  'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
   'href': 'https://api.spotify.com/v1/users/gjordj',
   'id': 'gjordj',
   'type': 'user',
   'uri': 'spotify:user:gjordj'},
  'is_local': False,
  'primary_color': None,
  'track': {'album': {'album_type': 'compilation',
    'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0LyfQWJT6nXafLPZqxe9Of'},
      'href': 'https://api.spotify.com/v1/artists/0LyfQWJT6nXafLPZqxe9Of',
      'id': '0LyfQWJT6nXafLPZqxe9Of',
      'name': 'Various Artists',
      'type': 'artist',
      'uri': 'spotify:artist:0LyfQWJT6nXafLPZqxe9Of'}],
    'available_markets': [],
    'external_urls': {'spotify': 'https://open.spotify.com/album/5p0H50uFCdWTpLY640HoPc'},
    'href': 'https://api.spotify.com/v1/albums/5p0H50uFCdWTpLY640HoPc',
    'id': '5p0H50uFCdWTpLY640HoPc',
    'images': [{'height': 640,
      'url': 'https://i.scdn.co/image/ab67616d0000b2732a307d64201d08144f68cc49',
      'width': 640},
     {'height': 300,
      'url': 'https://i.scdn.co/image/ab67616d00001e022a307d64201d08144f68cc49',
      'width': 300},
     {'height': 64,
      'url': 'https://i.scdn.co/image/ab67616d000048512a307d64201d08144f68cc49',
      'width': 64}],
    'name': 'La La Land (Original Motion Picture Soundtrack)',
    'release_date': '2016-12-09',
    'release_date_precision': 'day',
    'total_tracks': 15,
    'type': 'album',
    'uri': 'spotify:album:5p0H50uFCdWTpLY640HoPc'},
   'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/5y2Xq6xcjJb2jVM54GHK3t'},
     'href': 'https://api.spotify.com/v1/artists/5y2Xq6xcjJb2jVM54GHK3t',
     'id': '5y2Xq6xcjJb2jVM54GHK3t',
     'name': 'John Legend',
     'type': 'artist',
     'uri': 'spotify:artist:5y2Xq6xcjJb2jVM54GHK3t'}],
   'available_markets': [],
   'disc_number': 1,
   'duration_ms': 192093,
   'episode': False,
   'explicit': False,
   'external_ids': {'isrc': 'USUG11600664'},
   'external_urls': {'spotify': 'https://open.spotify.com/track/3gGHAScxUDsJqcAM6ZvjuH'},
   'href': 'https://api.spotify.com/v1/tracks/3gGHAScxUDsJqcAM6ZvjuH',
   'id': '3gGHAScxUDsJqcAM6ZvjuH',
   'is_local': False,
   'name': 'Start A Fire',
   'popularity': 5,
   'preview_url': None,
   'track': True,
   'track_number': 10,
   'type': 'track',
   'uri': 'spotify:track:3gGHAScxUDsJqcAM6ZvjuH'},
  'video_thumbnail': {'url': None}},
 {'added_at': '2017-12-05T13:44:23Z',
  'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
   'href': 'https://api.spotify.com/v1/users/gjordj',
   'id': 'gjordj',
   'type': 'user',
   'uri': 'spotify:user:gjordj'},
  'is_local': False,
  'primary_color': None,
  'track': {'album': {'album_type': 'album',
    'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0du5cEVh5yTK9QJze8zA0C'},
      'href': 'https://api.spotify.com/v1/artists/0du5cEVh5yTK9QJze8zA0C',
      'id': '0du5cEVh5yTK9QJze8zA0C',
      'name': 'Bruno Mars',
      'type': 'artist',
      'uri': 'spotify:artist:0du5cEVh5yTK9QJze8zA0C'}],
    'available_markets': ['AD',
     'AE',
     'AR',
     'AT',
     'AU',
     'BE',
     'BG',
     'BH',
     'BO',
     'BR',
     'CA',
     'CH',
     'CL',
     'CO',
     'CR',
     'CY',
     'CZ',
     'DE',
     'DK',
     'DO',
     'DZ',
     'EC',
     'EE',
     'EG',
     'ES',
     'FI',
     'FR',
     'GB',
     'GR',
     'GT',
     'HK',
     'HN',
     'HU',
     'ID',
     'IE',
     'IL',
     'IS',
     'IT',
     'JO',
     'JP',
     'KW',
     'LB',
     'LI',
     'LT',
     'LU',
     'LV',
     'MA',
     'MC',
     'MT',
     'MX',
     'MY',
     'NI',
     'NL',
     'NO',
     'NZ',
     'OM',
     'PA',
     'PE',
     'PH',
     'PL',
     'PS',
     'PT',
     'PY',
     'QA',
     'RO',
     'SA',
     'SE',
     'SG',
     'SK',
     'SV',
     'TH',
     'TN',
     'TR',
     'TW',
     'US',
     'UY',
     'VN',
     'ZA'],
    'external_urls': {'spotify': 'https://open.spotify.com/album/4PgleR09JVnm3zY1fW3XBA'},
    'href': 'https://api.spotify.com/v1/albums/4PgleR09JVnm3zY1fW3XBA',
    'id': '4PgleR09JVnm3zY1fW3XBA',
    'images': [{'height': 640,
      'url': 'https://i.scdn.co/image/ab67616d0000b273232711f7d66a1e19e89e28c5',
      'width': 640},
     {'height': 300,
      'url': 'https://i.scdn.co/image/ab67616d00001e02232711f7d66a1e19e89e28c5',
      'width': 300},
     {'height': 64,
      'url': 'https://i.scdn.co/image/ab67616d00004851232711f7d66a1e19e89e28c5',
      'width': 64}],
    'name': '24K Magic',
    'release_date': '2016-11-17',
    'release_date_precision': 'day',
    'total_tracks': 9,
    'type': 'album',
    'uri': 'spotify:album:4PgleR09JVnm3zY1fW3XBA'},
   'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0du5cEVh5yTK9QJze8zA0C'},
     'href': 'https://api.spotify.com/v1/artists/0du5cEVh5yTK9QJze8zA0C',
     'id': '0du5cEVh5yTK9QJze8zA0C',
     'name': 'Bruno Mars',
     'type': 'artist',
     'uri': 'spotify:artist:0du5cEVh5yTK9QJze8zA0C'}],
   'available_markets': ['AD',
    'AE',
    'AR',
    'AT',
    'AU',
    'BE',
    'BG',
    'BH',
    'BO',
    'BR',
    'CA',
    'CH',
    'CL',
    'CO',
    'CR',
    'CY',
    'CZ',
    'DE',
    'DK',
    'DO',
    'DZ',
    'EC',
    'EE',
    'EG',
    'ES',
    'FI',
    'FR',
    'GB',
    'GR',
    'GT',
    'HK',
    'HN',
    'HU',
    'ID',
    'IE',
    'IL',
    'IS',
    'IT',
    'JO',
    'JP',
    'KW',
    'LB',
    'LI',
    'LT',
    'LU',
    'LV',
    'MA',
    'MC',
    'MT',
    'MX',
    'MY',
    'NI',
    'NL',
    'NO',
    'NZ',
    'OM',
    'PA',
    'PE',
    'PH',
    'PL',
    'PS',
    'PT',
    'PY',
    'QA',
    'RO',
    'SA',
    'SE',
    'SG',
    'SK',
    'SV',
    'TH',
    'TN',
    'TR',
    'TW',
    'US',
    'UY',
    'VN',
    'ZA'],
   'disc_number': 1,
   'duration_ms': 225983,
   'episode': False,
   'explicit': False,
   'external_ids': {'isrc': 'USAT21602944'},
   'external_urls': {'spotify': 'https://open.spotify.com/track/6b8Be6ljOzmkOmFslEb23P'},
   'href': 'https://api.spotify.com/v1/tracks/6b8Be6ljOzmkOmFslEb23P',
   'id': '6b8Be6ljOzmkOmFslEb23P',
   'is_local': False,
   'name': '24K Magic',
   'popularity': 77,
   'preview_url': 'https://p.scdn.co/mp3-preview/3213a35ec32d4c747e794072a5ef7b464b4fcb69?cid=b43c7befbf6a4de29308d2dd70056f20',
   'track': True,
   'track_number': 1,
   'type': 'track',
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    'GB',
    'GR',
    'GT',
    'HK',
    'HN',
    'HU',
    'ID',
    'IE',
    'IL',
    'IS',
    'IT',
    'JO',
    'JP',
    'KW',
    'LB',
    'LI',
    'LT',
    'LU',
    'LV',
    'MA',
    'MC',
    'MT',
    'MX',
    'MY',
    'NI',
    'NL',
    'NO',
    'NZ',
    'OM',
    'PA',
    'PE',
    'PH',
    'PL',
    'PS',
    'PT',
    'PY',
    'QA',
    'RO',
    'SA',
    'SE',
    'SG',
    'SK',
    'SV',
    'TH',
    'TN',
    'TR',
    'TW',
    'US',
    'UY',
    'VN',
    'ZA'],
   'disc_number': 1,
   'duration_ms': 237533,
   'episode': False,
   'explicit': False,
   'external_ids': {'isrc': 'USAT29902109'},
   'external_urls': {'spotify': 'https://open.spotify.com/track/2dURQIBrw3XcHyVZlfdpC1'},
   'href': 'https://api.spotify.com/v1/tracks/2dURQIBrw3XcHyVZlfdpC1',
   'id': '2dURQIBrw3XcHyVZlfdpC1',
   'is_local': False,
   'name': 'Two of Hearts',
   'popularity': 53,
   'preview_url': 'https://p.scdn.co/mp3-preview/9426c57cd0292f6d5470520cee905828c8071a3c?cid=b43c7befbf6a4de29308d2dd70056f20',
   'track': True,
   'track_number': 1,
   'type': 'track',
   'uri': 'spotify:track:2dURQIBrw3XcHyVZlfdpC1'},
  'video_thumbnail': {'url': None}},
 ...]
In [9]:
for t in track_results:
    artist_name.append(t['track']['artists'][0]['name'])
    track_name.append(t['track']['name'])
    track_id.append(t['track']['id'])
    popularity.append(t['track']['popularity'])
    explicit.append(t['track']['explicit'])
    
print('number of elements in the track_id list:', len(track_id))

number of elements in the track_id list: 2342
In [10]:
dftracks = pd.DataFrame({'artist_name':artist_name,
                         'track_name':track_name,
                         'track_id':track_id,
                         'popularity':popularity,
                         'explicit':explicit})
In [11]:
#print(df_tracks.shape)
dftracks.shape
dftracks.info()
dftracks.head()#print(df_tracks.shape)

dftracks.shape
dftracks.info()
dftracks.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2342 entries, 0 to 2341
Data columns (total 5 columns):
artist_name    2342 non-null object
track_name     2342 non-null object
track_id       2342 non-null object
popularity     2342 non-null int64
explicit       2342 non-null bool
dtypes: bool(1), int64(1), object(3)
memory usage: 75.6+ KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2342 entries, 0 to 2341
Data columns (total 5 columns):
artist_name    2342 non-null object
track_name     2342 non-null object
track_id       2342 non-null object
popularity     2342 non-null int64
explicit       2342 non-null bool
dtypes: bool(1), int64(1), object(3)
memory usage: 75.6+ KB
Out[11]:
artist_name track_name track_id popularity explicit
0 La La Land Cast Another Day Of Sun - From "La La Land" Soundtrack 0zCfmceT1YvtZXGbR6Roht 8 False
1 John Legend Start A Fire 3gGHAScxUDsJqcAM6ZvjuH 5 False
2 Bruno Mars 24K Magic 6b8Be6ljOzmkOmFslEb23P 77 False
3 Major Lazer Cold Water (feat. Justin Bieber & MØ) 6DNtNfH8hXkqOX1sjqmI7p 19 False
4 Fort Frances Summertime 1dzk5fQIYJ3MMmgoRVw2qw 41 False
In [12]:
#SONG NAME DATA CLEANUP

grouped = dftracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped[grouped > 1].count()

dftracks.drop_duplicates(subset=['artist_name','track_name'], inplace=True)

# doing the same grouping as before to verify the solution
grouped_after_dropping = dftracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped_after_dropping[grouped_after_dropping > 1].count()

dftracks[dftracks.duplicated(subset=['artist_name','track_name'],keep=False)].count()
dftracks.shape

# MERGE BOTH DATAFRAMES

# the 'inner' method will make sure to keep track IDs present in both datasets
dftotal = pd.merge(dftracks,dfmet,on='track_id',how='inner')
dftotal.head()
dftotal.info()


dftotal[dftotal.duplicated(subset=['artist_name','track_name'],keep=False)]

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1162 entries, 0 to 1161
Data columns (total 18 columns):
artist_name         1162 non-null object
track_name          1162 non-null object
track_id            1162 non-null object
popularity          1162 non-null int64
explicit            1162 non-null bool
acousticness        1162 non-null float64
danceability        1162 non-null float64
duration_ms         1162 non-null int64
energy              1162 non-null float64
instrumentalness    1162 non-null float64
key                 1162 non-null int64
liveness            1162 non-null float64
loudness            1162 non-null float64
mode                1162 non-null int64
speechiness         1162 non-null float64
tempo               1162 non-null float64
time_signature      1162 non-null int64
valence             1162 non-null float64
dtypes: bool(1), float64(9), int64(5), object(3)
memory usage: 164.5+ KB
Out[12]:
artist_name track_name track_id popularity explicit acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
663 2Pac Do For Love 3dSGqXMll4UJUohLANG0ce 6 True 0.05160 0.861 281600 0.638 0.002240 10 0.0650 -3.878 0 0.3230 95.012 4 0.765
664 2Pac Do For Love 3dSGqXMll4UJUohLANG0ce 6 True 0.05160 0.861 281600 0.638 0.002240 10 0.0650 -3.878 0 0.3230 95.012 4 0.765
897 Enrique Iglesias Loco 0Hv8aeWmZt5vPngIql4UDF 1 False 0.37900 0.754 213667 0.860 0.000000 7 0.1110 -3.368 1 0.0285 132.964 4 0.874
898 Enrique Iglesias Loco 0Hv8aeWmZt5vPngIql4UDF 1 False 0.37900 0.754 213667 0.860 0.000000 7 0.1110 -3.368 1 0.0285 132.964 4 0.874
906 Pharrell Williams Happy - From "Despicable Me 2" 3RkvscbM4aNbELiQf7PJwn 13 False 0.29100 0.662 233293 0.757 0.000000 5 0.0885 -6.818 0 0.1440 159.945 4 0.962
907 Pharrell Williams Happy - From "Despicable Me 2" 3RkvscbM4aNbELiQf7PJwn 13 False 0.29100 0.662 233293 0.757 0.000000 5 0.0885 -6.818 0 0.1440 159.945 4 0.962
911 Faul & Wad Ad Changes 13qqdlSeF8FcxsRyapDMZ0 35 False 0.00864 0.822 345173 0.704 0.627000 3 0.0633 -8.242 0 0.0374 125.999 4 0.255
912 Faul & Wad Ad Changes 13qqdlSeF8FcxsRyapDMZ0 35 False 0.00864 0.822 345173 0.704 0.627000 3 0.0633 -8.242 0 0.0374 125.999 4 0.255
946 Elmore James Dust My Broom 3LGB6BkiSmzP8IHo03rZMo 39 False 0.24700 0.526 174667 0.664 0.012400 2 0.2890 -6.620 1 0.0354 101.928 4 0.797
947 Elmore James Dust My Broom 3LGB6BkiSmzP8IHo03rZMo 39 False 0.24700 0.526 174667 0.664 0.012400 2 0.2890 -6.620 1 0.0354 101.928 4 0.797
950 Cannonball Adderley Mercy, Mercy, Mercy 7aBKUvEYylCA24Cn8vepJj 28 False 0.70200 0.398 308507 0.275 0.151000 10 0.9780 -16.175 1 0.0386 170.076 4 0.436
951 Cannonball Adderley Mercy, Mercy, Mercy 7aBKUvEYylCA24Cn8vepJj 28 False 0.70200 0.398 308507 0.275 0.151000 10 0.9780 -16.175 1 0.0386 170.076 4 0.436
952 Ian Dury Hit Me With Your Rhythm Stick 4txFRJmpBJCRfDIAc30yTB 26 True 0.07390 0.713 223133 0.861 0.001730 5 0.1140 -7.834 0 0.0360 104.299 4 0.898
953 Ian Dury Hit Me With Your Rhythm Stick 4txFRJmpBJCRfDIAc30yTB 26 True 0.07390 0.713 223133 0.861 0.001730 5 0.1140 -7.834 0 0.0360 104.299 4 0.898
954 Clyde McCoy Tear It Down 3FZ43CoJqo5iU4XQ2zwZCD 12 False 0.98200 0.875 183493 0.425 0.116000 5 0.1080 -11.124 1 0.0915 114.792 4 0.874
955 Clyde McCoy Tear It Down 3FZ43CoJqo5iU4XQ2zwZCD 12 False 0.98200 0.875 183493 0.425 0.116000 5 0.1080 -11.124 1 0.0915 114.792 4 0.874
957 Plastic Bertrand Ca plane pour moi 71yCMlsD6qbD7NmNUEoVNR 59 False 0.07400 0.422 182133 0.943 0.000068 10 0.1050 -4.365 1 0.0954 165.764 4 0.845
958 Plastic Bertrand Ca plane pour moi 71yCMlsD6qbD7NmNUEoVNR 59 False 0.07400 0.422 182133 0.943 0.000068 10 0.1050 -4.365 1 0.0954 165.764 4 0.845
960 Charles Mingus Wednesday Night Prayer Meeting 74EiaLmtaOlfKZYFkhHs6J 30 False 0.68700 0.364 342480 0.470 0.002540 5 0.0849 -13.051 0 0.0730 86.900 4 0.486
961 Charles Mingus Wednesday Night Prayer Meeting 74EiaLmtaOlfKZYFkhHs6J 30 False 0.68700 0.364 342480 0.470 0.002540 5 0.0849 -13.051 0 0.0730 86.900 4 0.486
1038 Sandy Marton People from Ibiza 0OcAaaQfHvawA7F0n6Wha6 2 False 0.01820 0.722 306360 0.705 0.318000 7 0.0579 -9.156 1 0.0684 119.525 4 0.454
1039 Sandy Marton People from Ibiza 0OcAaaQfHvawA7F0n6Wha6 2 False 0.01820 0.722 306360 0.705 0.318000 7 0.0579 -9.156 1 0.0684 119.525 4 0.454
1048 José de Rico Soltera (feat. Danny Romero & Fito Blanko) 58xwJEkzxhyXzYyjELtOCN 43 False 0.04210 0.629 172493 0.938 0.000000 0 0.2760 -4.335 1 0.0471 128.031 4 0.949
1049 José de Rico Soltera (feat. Danny Romero & Fito Blanko) 58xwJEkzxhyXzYyjELtOCN 43 False 0.04210 0.629 172493 0.938 0.000000 0 0.2760 -4.335 1 0.0471 128.031 4 0.949
1052 The Whispers And The Beat Goes On 77OW25gumw4UBDvhXDVWdQ 1 False 0.18100 0.804 205467 0.530 0.000339 11 0.0694 -9.645 0 0.1390 115.226 4 0.873
1053 The Whispers And The Beat Goes On 77OW25gumw4UBDvhXDVWdQ 1 False 0.18100 0.804 205467 0.530 0.000339 11 0.0694 -9.645 0 0.1390 115.226 4 0.873
1061 The Angels My Boyfriend's Back 4jO6bQ15aDnMQplLL04cSy 8 False 0.14700 0.759 127093 0.933 0.008020 3 0.1110 -5.293 1 0.2170 135.458 4 0.801
1062 The Angels My Boyfriend's Back 4jO6bQ15aDnMQplLL04cSy 8 False 0.14700 0.759 127093 0.933 0.008020 3 0.1110 -5.293 1 0.2170 135.458 4 0.801
In [13]:
#write to csv
dftotal.to_csv('../csv_files/datasetPlaylist.csv')
dfPlaylist = dftotal
dfPlaylist.head()
Out[13]:
artist_name track_name track_id popularity explicit acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 La La Land Cast Another Day Of Sun - From "La La Land" Soundtrack 0zCfmceT1YvtZXGbR6Roht 8 False 0.0180 0.584 228173 0.752 0.000004 8 0.647 -6.689 1 0.0580 125.797 4 0.791
1 John Legend Start A Fire 3gGHAScxUDsJqcAM6ZvjuH 5 False 0.0834 0.543 192093 0.848 0.000000 10 0.799 -6.058 0 0.0531 102.009 4 0.290
2 Bruno Mars 24K Magic 6b8Be6ljOzmkOmFslEb23P 77 False 0.0340 0.818 225983 0.803 0.000000 1 0.153 -4.282 1 0.0797 106.970 4 0.632
3 Major Lazer Cold Water (feat. Justin Bieber & MØ) 6DNtNfH8hXkqOX1sjqmI7p 19 False 0.0736 0.608 185352 0.798 0.000000 6 0.156 -5.092 0 0.0432 92.943 4 0.501
4 Fort Frances Summertime 1dzk5fQIYJ3MMmgoRVw2qw 41 False 0.1010 0.707 254739 0.594 0.002140 5 0.122 -7.461 1 0.0470 88.000 4 0.193
In [14]:
len(dfPlaylist)
Out[14]:
1162

Go to Table of Contents

Data Set Train from Year Random Sample

In [15]:
#GET SONG NAMES OF RANDOM SAMPLE
#####################################
sample_size = 5000 # number of songs per 'year'

#####################################
random.seed(435)
# create empty lists where the results are going to be stored
artist_name = []
track_name = []
popularity = []
track_id = []
explicit = []
year_song = []
seedIter = 0
yearNumber = ['year:2008', 'year:2009', 'year:2010', 'year:2011', 'year:2012',
              'year:2013','year:2014','year:2015','year:2016',
              'year:2017', 'year:2018', 'year:2019']


#random sample. set sample size, sample rate max 100, and song year

for j in yearNumber:
    random.seed(seedIter)
    for i in range(0,sample_size,10):
        track_results = sp.search(q= j, type='track', limit=10,offset=i)
        for i, t in enumerate(track_results['tracks']['items']):
            artist_name.append(t['artists'][0]['name'])
            track_name.append(t['name'])
            track_id.append(t['id'])
            popularity.append(t['popularity'])
            explicit.append(t["explicit"])
            year_song.append(int(j[-4:]))
    seedIter = seedIter + 1

print('number of elements in the track_id list:', len(track_id))
#imports track names
import pandas as pd
df_tracks = pd.DataFrame({'artist_name':artist_name,
                          'track_name':track_name,
                          'track_id':track_id,
                          'popularity':popularity,
                          'explicit':explicit,
                          'year_song':year_song})
print(df_tracks.shape)
len(df_tracks)

number of elements in the track_id list: 60000
(60000, 6)
Out[15]:
60000
In [16]:
#SONG NAME DATA CLEANUP

grouped = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped[grouped > 1].count()

df_tracks.drop_duplicates(subset=['artist_name','track_name'], inplace=True)

# doing the same grouping as before to verify the solution
grouped_after_dropping = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped_after_dropping[grouped_after_dropping > 1].count()

df_tracks[df_tracks.duplicated(subset=['artist_name','track_name'],keep=False)].count()
df_tracks.shape
Out[16]:
(56653, 6)
In [17]:
#GET SONG METRICS OF THE RANDOM SAMPLE


# empty list, batchsize and the counter for None results
rows = []
batchsize = 50
None_counter = 0


for i in range(0,len(df_tracks['track_id']),batchsize):
    batch = df_tracks['track_id'][i:i+batchsize]
    feature_results = sp.audio_features(batch)
    for i, t in enumerate(feature_results):
        if t == None:
            None_counter = None_counter + 1
        else:
            rows.append(t)
            
            
print('Number of tracks where no audio features were available:',None_counter)
print('number of elements in the track_id list:', len(rows))


df_audio_features = pd.DataFrame.from_dict(rows,orient='columns')
print("Shape of the dataset:", df_audio_features.shape)
df_audio_features.head()
df_audio_features.info()

Number of tracks where no audio features were available: 1
number of elements in the track_id list: 56652
Shape of the dataset: (56652, 18)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56652 entries, 0 to 56651
Data columns (total 18 columns):
acousticness        56652 non-null float64
analysis_url        56652 non-null object
danceability        56652 non-null float64
duration_ms         56652 non-null int64
energy              56652 non-null float64
id                  56652 non-null object
instrumentalness    56652 non-null float64
key                 56652 non-null int64
liveness            56652 non-null float64
loudness            56652 non-null float64
mode                56652 non-null int64
speechiness         56652 non-null float64
tempo               56652 non-null float64
time_signature      56652 non-null int64
track_href          56652 non-null object
type                56652 non-null object
uri                 56652 non-null object
valence             56652 non-null float64
dtypes: float64(9), int64(4), object(5)
memory usage: 7.8+ MB
In [18]:
#SONG METRICS DATA CLEANUP

columns_to_drop = ['analysis_url','track_href','type','uri']
df_audio_features.drop(columns_to_drop, axis=1,inplace=True)

df_audio_features.rename(columns={'id': 'track_id'}, inplace=True)

df_audio_features.shape
Out[18]:
(56652, 14)
In [19]:
# MERGE BOTH DATAFRAMES

# the 'inner' method will make sure that we only keep track IDs present in both datasets
df = pd.merge(df_tracks,df_audio_features,on='track_id',how='inner')
print("Shape of the dataset:", df_audio_features.shape)
df.head()
df.info()

df[df.duplicated(subset=['artist_name','track_name'],keep=False)]

Shape of the dataset: (56652, 14)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 56652 entries, 0 to 56651
Data columns (total 19 columns):
artist_name         56652 non-null object
track_name          56652 non-null object
track_id            56652 non-null object
popularity          56652 non-null int64
explicit            56652 non-null bool
year_song           56652 non-null int64
acousticness        56652 non-null float64
danceability        56652 non-null float64
duration_ms         56652 non-null int64
energy              56652 non-null float64
instrumentalness    56652 non-null float64
key                 56652 non-null int64
liveness            56652 non-null float64
loudness            56652 non-null float64
mode                56652 non-null int64
speechiness         56652 non-null float64
tempo               56652 non-null float64
time_signature      56652 non-null int64
valence             56652 non-null float64
dtypes: bool(1), float64(9), int64(6), object(3)
memory usage: 8.3+ MB
Out[19]:
artist_name track_name track_id popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
In [20]:
#WRITE TO CSV

df.to_csv('../csv_files/datasetYearRandom.csv')
In [21]:
dfYearRandom = df
dfYearRandom.head()
Out[21]:
artist_name track_name track_id popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 Jason Mraz I'm Yours 1EzrEOXmMH3G43AXT1y7pA 82 False 2008 0.59500 0.686 242187 0.457 0.0 11 0.105 -8.322 1 0.0468 150.953 4 0.718
1 Zac Brown Band Chicken Fried 4dGJf1SER1T6ooX46vwzRB 75 False 2008 0.64500 0.566 238147 0.713 0.0 6 0.114 -4.250 1 0.0417 169.864 4 0.807
2 Estelle American Boy 22UDw8rSfLbUsaAGTXQ4Z8 79 True 2008 0.17100 0.727 284733 0.729 0.0 0 0.070 -2.990 1 0.3260 117.932 4 0.512
3 Kanye West Heartless 4EWCNWgDS8707fNSZ1oaA5 75 False 2008 0.05150 0.790 211000 0.647 0.0 10 0.248 -5.983 0 0.1360 87.999 4 0.654
4 The Offspring You're Gonna Go Far, Kid 6TfBA04WJ3X1d1wXhaCFVT 77 True 2008 0.00428 0.550 177827 0.917 0.0 0 0.197 -3.159 1 0.0638 126.115 4 0.601

Go to Table of Contents

Data Set Test from Year Random Sample

In [22]:
#GET SONG NAMES OF RANDOM SAMPLE
#####################################
sample_size = 1000 # number of songs#
#####################################
random.seed(123)
# create empty lists where the results are going to be stored
artist_name = []
track_name = []
popularity = []
track_id = []
explicit = []
year_song = []
#random sample. set sample size, sample rate max 100, and song year
for i in range(0,sample_size,10):
    track_results = sp.search(q='year:2019', type='track', limit=10,offset=i)
    for i, t in enumerate(track_results['tracks']['items']):
        artist_name.append(t['artists'][0]['name'])
        track_name.append(t['name'])
        track_id.append(t['id'])
        popularity.append(t['popularity'])
        explicit.append(t["explicit"])
        year_song.append(int(j[-4:]))
                         
print('number of elements in the track_id list:', len(track_id))
#imports track names
import pandas as pd
df_tracks = pd.DataFrame({'artist_name':artist_name,
                          'track_name':track_name,
                          'track_id':track_id,
                          'popularity':popularity,
                          'explicit':explicit,
                          'year_song':year_song})
print(df_tracks.shape)
df_tracks.head()

number of elements in the track_id list: 1000
(1000, 6)
Out[22]:
artist_name track_name track_id popularity explicit year_song
0 Roddy Ricch The Box 0nbXyq5TXYPCO7pr3N8S4I 100 True 2019
1 Arizona Zervas ROXANNE 696DnlkuDOXcMAnKlTgXXK 99 True 2019
2 Post Malone Circles 21jGcNKet2qwijlDFuPiPb 97 False 2019
3 DaBaby BOP 6Ozh9Ok6h4Oi1wUSLtBseN 93 True 2019
4 Juice WRLD Bandit (with YoungBoy Never Broke Again) 6Gg1gjgKi2AK4e0qzsR7sd 92 True 2019
In [23]:
#SONG NAME DATA CLEANUP

grouped = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped[grouped > 1].count()

df_tracks.drop_duplicates(subset=['artist_name','track_name'], inplace=True)

# doing the same grouping as before to verify the solution
grouped_after_dropping = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped_after_dropping[grouped_after_dropping > 1].count()

df_tracks[df_tracks.duplicated(subset=['artist_name','track_name'],keep=False)].count()
df_tracks.shape
Out[23]:
(962, 6)
In [24]:
#GET SONG METRICS OF THE RANDOM SAMPLE


# empty list, batchsize and the counter for None results
rows = []
batchsize = 50
None_counter = 0


for i in range(0,len(df_tracks['track_id']),batchsize):
    batch = df_tracks['track_id'][i:i+batchsize]
    feature_results = sp.audio_features(batch)
    for i, t in enumerate(feature_results):
        if t == None:
            None_counter = None_counter + 1
        else:
            rows.append(t)
            
            
print('Number of tracks where no audio features were available:',None_counter)
print('number of elements in the track_id list:', len(rows))


df_audio_features = pd.DataFrame.from_dict(rows,orient='columns')
print("Shape of the dataset:", df_audio_features.shape)
df_audio_features.head()
df_audio_features.info()

Number of tracks where no audio features were available: 0
number of elements in the track_id list: 962
Shape of the dataset: (962, 18)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 962 entries, 0 to 961
Data columns (total 18 columns):
acousticness        962 non-null float64
analysis_url        962 non-null object
danceability        962 non-null float64
duration_ms         962 non-null int64
energy              962 non-null float64
id                  962 non-null object
instrumentalness    962 non-null float64
key                 962 non-null int64
liveness            962 non-null float64
loudness            962 non-null float64
mode                962 non-null int64
speechiness         962 non-null float64
tempo               962 non-null float64
time_signature      962 non-null int64
track_href          962 non-null object
type                962 non-null object
uri                 962 non-null object
valence             962 non-null float64
dtypes: float64(9), int64(4), object(5)
memory usage: 135.4+ KB
In [25]:
#SONG METRICS DATA CLEANUP

columns_to_drop = ['analysis_url','track_href','type','uri']
df_audio_features.drop(columns_to_drop, axis=1,inplace=True)

df_audio_features.rename(columns={'id': 'track_id'}, inplace=True)

df_audio_features.shape
Out[25]:
(962, 14)
In [26]:
# the 'inner' method will make sure that we only keep track IDs present in both datasets
dfTest = pd.merge(df_tracks,df_audio_features,on='track_id',how='inner')
print("Shape of the dataset:", df_audio_features.shape)
dfTest.head()
dfTest.info()
dfTest[dfTest.duplicated(subset=['artist_name','track_name'],keep=False)]

Shape of the dataset: (962, 14)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 962 entries, 0 to 961
Data columns (total 19 columns):
artist_name         962 non-null object
track_name          962 non-null object
track_id            962 non-null object
popularity          962 non-null int64
explicit            962 non-null bool
year_song           962 non-null int64
acousticness        962 non-null float64
danceability        962 non-null float64
duration_ms         962 non-null int64
energy              962 non-null float64
instrumentalness    962 non-null float64
key                 962 non-null int64
liveness            962 non-null float64
loudness            962 non-null float64
mode                962 non-null int64
speechiness         962 non-null float64
tempo               962 non-null float64
time_signature      962 non-null int64
valence             962 non-null float64
dtypes: bool(1), float64(9), int64(6), object(3)
memory usage: 143.7+ KB
Out[26]:
artist_name track_name track_id popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
In [27]:
#WRITE TO CSV

dfTest.to_csv('../csv_files/datasetTestYearRandom.csv')
In [28]:
dfTestYearRandom = dfTest
dfTestYearRandom.head()
Out[28]:
artist_name track_name track_id popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 Roddy Ricch The Box 0nbXyq5TXYPCO7pr3N8S4I 100 True 2019 0.1040 0.896 196653 0.586 0.00000 10 0.7900 -6.687 0 0.0559 116.971 4 0.642
1 Arizona Zervas ROXANNE 696DnlkuDOXcMAnKlTgXXK 99 True 2019 0.0522 0.621 163636 0.601 0.00000 6 0.4600 -5.616 0 0.1480 116.735 5 0.457
2 Post Malone Circles 21jGcNKet2qwijlDFuPiPb 97 False 2019 0.1920 0.695 215280 0.762 0.00244 0 0.0863 -3.497 1 0.0395 120.042 4 0.553
3 DaBaby BOP 6Ozh9Ok6h4Oi1wUSLtBseN 93 True 2019 0.1890 0.769 159715 0.787 0.00000 11 0.1290 -3.909 1 0.3670 126.770 4 0.836
4 Juice WRLD Bandit (with YoungBoy Never Broke Again) 6Gg1gjgKi2AK4e0qzsR7sd 92 True 2019 0.0687 0.474 189323 0.631 0.00000 5 0.1320 -5.884 0 0.3430 180.051 4 0.425

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Data Wrangling

Go to Table of Contents

Data Wrangling 1: Encoding of Target Variable: 'popularity'

Train Data Set

In [29]:
dfTraining = dfYearRandom
In [30]:
# CHOOSE:
    # dfYearRandom
    # dfPlaylist
    # dfTraining

dfEncodedTarget = dfTraining

bins = [0,10,20,30,40,50,60,70,80,90,100]
labels=[0,1,2,3,4,5,6,7,8,9]
dfEncodedTarget['popularity'] = pd.cut(dfEncodedTarget['popularity'], bins=bins, labels=labels, include_lowest=True)
dfEncodedTarget.head()
Out[30]:
artist_name track_name track_id popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 Jason Mraz I'm Yours 1EzrEOXmMH3G43AXT1y7pA 8 False 2008 0.59500 0.686 242187 0.457 0.0 11 0.105 -8.322 1 0.0468 150.953 4 0.718
1 Zac Brown Band Chicken Fried 4dGJf1SER1T6ooX46vwzRB 7 False 2008 0.64500 0.566 238147 0.713 0.0 6 0.114 -4.250 1 0.0417 169.864 4 0.807
2 Estelle American Boy 22UDw8rSfLbUsaAGTXQ4Z8 7 True 2008 0.17100 0.727 284733 0.729 0.0 0 0.070 -2.990 1 0.3260 117.932 4 0.512
3 Kanye West Heartless 4EWCNWgDS8707fNSZ1oaA5 7 False 2008 0.05150 0.790 211000 0.647 0.0 10 0.248 -5.983 0 0.1360 87.999 4 0.654
4 The Offspring You're Gonna Go Far, Kid 6TfBA04WJ3X1d1wXhaCFVT 7 True 2008 0.00428 0.550 177827 0.917 0.0 0 0.197 -3.159 1 0.0638 126.115 4 0.601

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Percentage of popularity types

In [31]:
popularityPercentage = dfEncodedTarget.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()
tablePopularity
Out[31]:
popularity
0    0.000000
1    0.000000
2    0.000459
3    0.155776
4    0.329150
5    0.308374
6    0.160100
7    0.039293
8    0.006266
9    0.000583
Name: popularity, dtype: float64
In [32]:
tablePopularity.plot.bar()
plt.show()

Test Data Set

In [33]:
bins = [0,10,20,30,40,50,60,70,80,90,100]
labels=[0,1,2,3,4,5,6,7,8,9]
len(bins),len(labels)
Out[33]:
(11, 10)
In [34]:
bins = [0,10,20,30,40,50,60,70,80,90,100]
labels=[0,1,2,3,4,5,6,7,8,9]
dfTestYearRandom['popularity'] = pd.cut(dfTestYearRandom['popularity'], bins=bins, labels=labels, include_lowest=True)
dfTestYearRandom.head()
Out[34]:
artist_name track_name track_id popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 Roddy Ricch The Box 0nbXyq5TXYPCO7pr3N8S4I 9 True 2019 0.1040 0.896 196653 0.586 0.00000 10 0.7900 -6.687 0 0.0559 116.971 4 0.642
1 Arizona Zervas ROXANNE 696DnlkuDOXcMAnKlTgXXK 9 True 2019 0.0522 0.621 163636 0.601 0.00000 6 0.4600 -5.616 0 0.1480 116.735 5 0.457
2 Post Malone Circles 21jGcNKet2qwijlDFuPiPb 9 False 2019 0.1920 0.695 215280 0.762 0.00244 0 0.0863 -3.497 1 0.0395 120.042 4 0.553
3 DaBaby BOP 6Ozh9Ok6h4Oi1wUSLtBseN 9 True 2019 0.1890 0.769 159715 0.787 0.00000 11 0.1290 -3.909 1 0.3670 126.770 4 0.836
4 Juice WRLD Bandit (with YoungBoy Never Broke Again) 6Gg1gjgKi2AK4e0qzsR7sd 9 True 2019 0.0687 0.474 189323 0.631 0.00000 5 0.1320 -5.884 0 0.3430 180.051 4 0.425

Percentage of popularity types

In [35]:
popularityPercentage = dfTestYearRandom.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()

tablePopularity.plot.bar()
plt.show()
In [36]:
len(dfTestYearRandom)/(len(dfTraining)+len(dfTestYearRandom))
Out[36]:
0.0166973305099455

Super unbalanced Test Data Set: New Strategy for Train and Test

Given that the API only provide a specific sample of songs, the levels of popularity is limited; so, we need this adjustment to make every model work.

In [37]:
len(bins), len(labels)
Out[37]:
(11, 10)
In [38]:
bins = range(min(dfEncodedTarget['popularity']),max(dfEncodedTarget['popularity'])+1)
labels = []
for i in bins[:-1]:
        labels.append(i- min(dfEncodedTarget['popularity']))

dfEncodedTarget['popularity'] = pd.cut(dfEncodedTarget['popularity'], bins=bins, labels=labels, include_lowest=True)
dfEncodedTarget.head()
Out[38]:
artist_name track_name track_id popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 Jason Mraz I'm Yours 1EzrEOXmMH3G43AXT1y7pA 5 False 2008 0.59500 0.686 242187 0.457 0.0 11 0.105 -8.322 1 0.0468 150.953 4 0.718
1 Zac Brown Band Chicken Fried 4dGJf1SER1T6ooX46vwzRB 4 False 2008 0.64500 0.566 238147 0.713 0.0 6 0.114 -4.250 1 0.0417 169.864 4 0.807
2 Estelle American Boy 22UDw8rSfLbUsaAGTXQ4Z8 4 True 2008 0.17100 0.727 284733 0.729 0.0 0 0.070 -2.990 1 0.3260 117.932 4 0.512
3 Kanye West Heartless 4EWCNWgDS8707fNSZ1oaA5 4 False 2008 0.05150 0.790 211000 0.647 0.0 10 0.248 -5.983 0 0.1360 87.999 4 0.654
4 The Offspring You're Gonna Go Far, Kid 6TfBA04WJ3X1d1wXhaCFVT 4 True 2008 0.00428 0.550 177827 0.917 0.0 0 0.197 -3.159 1 0.0638 126.115 4 0.601
In [39]:
msk = np.random.rand(len(dfEncodedTarget)) < 0.8

dfEncodedTarget = df[msk]

dfTestYearRandom = df[~msk]

len(dfTestYearRandom)/(len(dfEncodedTarget)+len(dfTestYearRandom))
Out[39]:
0.1975040598743204

Percentage of popularity types for Train

In [40]:
popularityPercentage = dfEncodedTarget.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()

tablePopularity.plot.bar()
plt.show()
In [41]:
tablePopularity.mean()
Out[41]:
0.14285714285714285

Percentage of popularity types for Test

In [42]:
popularityPercentage = dfTestYearRandom.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()

tablePopularity.plot.bar()
plt.show()

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Data Wrangling 2: Encoding of 'Explicit' and Selection of Numeric Variables

Train Data Set

In [43]:
dfEncoded = dfEncodedTarget
dfEncoded['explicit'] = dfEncoded['explicit'].map(lambda x: 0 if x == False else 1)

/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [44]:
dfEncoded = dfEncoded.iloc[:,3:]
In [45]:
dfEncoded.head()
Out[45]:
popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 5 0 2008 0.59500 0.686 242187 0.457 0.0 11 0.1050 -8.322 1 0.0468 150.953 4 0.718
1 4 0 2008 0.64500 0.566 238147 0.713 0.0 6 0.1140 -4.250 1 0.0417 169.864 4 0.807
2 4 1 2008 0.17100 0.727 284733 0.729 0.0 0 0.0700 -2.990 1 0.3260 117.932 4 0.512
4 4 1 2008 0.00428 0.550 177827 0.917 0.0 0 0.1970 -3.159 1 0.0638 126.115 4 0.601
5 4 0 2008 0.27200 0.508 261640 0.720 0.0 11 0.0563 -5.908 0 0.0628 79.983 4 0.472

Test Data Set

In [46]:
dfEncodedTest = dfTestYearRandom
dfEncodedTest['explicit'] = dfEncodedTest['explicit'].map(lambda x: 0 if x == False else 1)
dfEncodedTest = dfEncodedTest.iloc[:,3:]
dfEncodedTest.head()

/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Out[46]:
popularity explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
3 4 0 2008 0.0515 0.790 211000 0.647 0.000000 10 0.2480 -5.983 0 0.1360 87.999 4 0.654
6 4 0 2008 0.0941 0.485 242373 0.619 0.000003 5 0.1090 -7.115 0 0.0289 138.017 4 0.416
7 4 0 2008 0.1730 0.630 261427 0.698 0.000000 10 0.0789 -4.510 1 0.0244 94.023 4 0.529
10 4 1 2008 0.0387 0.673 221840 0.695 0.002060 6 0.1940 -8.636 0 0.2780 151.468 4 0.774
11 4 1 2008 0.0161 0.680 249533 0.687 0.000000 9 0.2610 -6.162 0 0.0709 150.053 4 0.467

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Data Wrangling 3:

Train Data

Scaling:

In [47]:
# INPUT: dfEncoded
# OUTPUT: predictiveVariablesScaled data scaled by MinMaxScaler to prepare data for the Lasso
        # popularityTarget 

scaler = MinMaxScaler()
predictiveVariablesScaled = pd.DataFrame(scaler.fit_transform(dfEncoded.iloc[:,1:]), columns=dfEncoded.iloc[:,1:].columns)
popularityTarget = dfEncoded['popularity']
predictiveVariablesScaled.head()
Out[47]:
explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 0.0 0.0 0.597390 0.695740 0.043541 0.456989 0.0 1.000000 0.1050 0.824296 1.0 0.048750 0.648730 0.8 0.720160
1 0.0 0.0 0.647590 0.574037 0.042792 0.712994 0.0 0.545455 0.1140 0.896456 1.0 0.043438 0.730001 0.8 0.809428
2 1.0 0.0 0.171687 0.737323 0.051425 0.728995 0.0 0.000000 0.0700 0.918784 1.0 0.339583 0.506820 0.8 0.513541
3 1.0 0.0 0.004297 0.557809 0.031614 0.916998 0.0 0.000000 0.1970 0.915789 1.0 0.066458 0.541987 0.8 0.602808
4 0.0 0.0 0.273092 0.515213 0.047146 0.719994 0.0 1.000000 0.0563 0.867074 0.0 0.065417 0.343732 0.8 0.473420

Test Data

Scaling:

In [48]:
# INPUT: dfEncoded
# OUTPUT: predictiveVariablesScaled data scaled by MinMaxScaler to prepare data for the Lasso
        # popularityTarget 

scaler = MinMaxScaler()
predictiveVariablesScaledTest = pd.DataFrame(scaler.fit_transform(dfEncodedTest.iloc[:,1:]), columns=dfEncodedTest.iloc[:,1:].columns)
popularityTargetTest = dfEncodedTest['popularity']
predictiveVariablesScaledTest.head()
Out[48]:
explicit year_song acousticness danceability duration_ms energy instrumentalness key liveness loudness mode speechiness tempo time_signature valence
0 0.0 0.0 0.051707 0.800405 0.044181 0.647641 0.000000 0.909091 0.251777 0.882984 0.0 0.140787 0.401216 0.8 0.659274
1 0.0 0.0 0.094478 0.491388 0.051852 0.619612 0.000003 0.454545 0.110660 0.862956 0.0 0.029917 0.629264 0.8 0.419355
2 0.0 0.0 0.173695 0.638298 0.056511 0.698693 0.000000 0.909091 0.080102 0.909044 1.0 0.025259 0.428681 0.8 0.533266
3 1.0 0.0 0.038855 0.681864 0.046832 0.695690 0.002060 0.545455 0.196954 0.836046 0.0 0.287785 0.690591 0.8 0.780242
4 1.0 0.0 0.016165 0.688956 0.053603 0.687682 0.000000 0.818182 0.264975 0.879817 0.0 0.073395 0.684139 0.8 0.470766

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Lasso

What LASSO does well is to provide a principled way to reduce the number of features in a model. LASSO involves a penalty factor that determines how many features are retained; using cross-validation to choose the penalty factor helps assure that the model will generalize well to future data samples.

If you need to cut down on the number of predictors for practical reasons, LASSO is a good choice. But all it does is give you a useful set of selected predictors, not necessarily the most important in some general sense.

When features are correlated, LASSO will choose one or the other based on its performance in the particular data sample at hand. With a different sample it could well choose a different feature from a set of correlated features.

This is why we are interested in using Lasso for the complete data set.

In [49]:
# INPUT: predictiveVariablesScaled, variables scaled and prepared for the Lasso and popularityTarget
# OUTPUT: 
    # 'cs',the array of penalties for the Lasso method
    # depending on the variables selected.

start_time = time.time()

cs = l1_min_c(predictiveVariablesScaled, popularityTarget, loss='log') * np.logspace(0, 7, 16)

#We use the Logistic Regression to apply the penalties from the Lasso.
clf = linear_model.LogisticRegression(penalty='l1', solver='saga',
                                      tol=1e-6, max_iter=int(1e6),
                                      warm_start=True,
                                     multi_class= 'multinomial')
print("--- %s seconds ---" % (time.time() - start_time))

--- 0.01909017562866211 seconds ---
In [50]:
# ************************************************
# ATTENTION!!!! 763.1 s           Executing time!!*
# ************************************************
# INPUT: predictiveVariables, variables scaled and prepared for the Lasso and popularityTarget
# OUTPUT: fit the linear model to minimize the error function
        # for graphical purpose and see how Lasso selects the variables.
    
start_time = time.time()

coefs_ = []
for c in cs:
    clf.set_params(C=c)
    clf.fit(X = predictiveVariablesScaled, y = popularityTarget)
    coefs_.append(clf.coef_.ravel().copy())
    
print("--- %s seconds ---" % (time.time() - start_time))

--- 763.1988909244537 seconds ---
In [51]:
coefs_ = np.array(coefs_)
plt.plot(np.log10(cs), coefs_, marker='o')
ymin, ymax  = plt.ylim()
plt.xlabel('log(C)')
plt.ylabel('Coefficients')
plt.title('Logistic Regression Path')
plt.axis('tight')
plt.show()
In [52]:
# INPUT: predictiveVariablesScaled and clf, the linear_model.LogisticRegression
# OUTPUT: lassoVariablesFilter, it has all the variables with its corresponding coefficient given by the Lasso.
    # If the coefficient == o, it means it shouldn't be included in the model.
    
start_time = time.time()
lassoVariablesFilter = []
# for coefs in range(0,clf.coef_.size):
for coefs in range(0,predictiveVariablesScaled.shape[1]):
    lassoVariablesFilter.append((predictiveVariablesScaled.columns[coefs], clf.coef_[0][coefs]))
    
print("--- %s seconds ---" % (time.time() - start_time))

--- 0.0003440380096435547 seconds ---
In [53]:
# INPUT:
    # lassoVariablesFilter, , it has all the variables with its corresponding coefficient given by the Lasso.
    # lassoVars,data of all the variables scaled by MinMaxScaler to prepare data for the Lasso
    # numericaAndCategoricalVariablesDataframe, dataframe with all variables.
    
# OUTPUT: lassoVariablesSelectedDataFrame, data frame with the data of the variables selected by the Lasso and scaled.
        # lassoVariablesSelectedNonScaledDataFrame, , data frame with the data of the variables selected by the Lasso.
start_time = time.time()
out_tup = [i for i in lassoVariablesFilter if i[1] == 0]
lassoVariablesSelected = [i for i in lassoVariablesFilter if i[1] != 0]

lassoVariablesSelectedDataFrame = predictiveVariablesScaled[list(map(lambda x: x[0] , lassoVariablesSelected))]
lassoVariablesSelectedNonScaledDataFrame = dfEncoded.iloc[:,1:][list(map(lambda x: x[0] , lassoVariablesSelected))]
print("--- %s seconds ---" % (time.time() - start_time))

--- 0.01439213752746582 seconds ---

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Final Variables

All variables are important based on the Lasso model.

In [54]:
lassoVariablesSelected, len(lassoVariablesSelected)
Out[54]:
([('explicit', -0.07153324599907189),
  ('year_song', -9.702644827469165),
  ('acousticness', -0.3254471074277977),
  ('danceability', -1.2073589518844228),
  ('duration_ms', -2.8156027992892434),
  ('energy', 0.01184728627651793),
  ('instrumentalness', 0.3899812786757341),
  ('key', 0.041241030799594784),
  ('liveness', 0.25354062649132436),
  ('loudness', -0.6421474270361321),
  ('mode', 0.24023488849848687),
  ('speechiness', 1.9882322610031846),
  ('tempo', -0.08274000034546936),
  ('time_signature', -0.4471910261268723),
  ('valence', -0.008273612271184968)],
 15)

The regularization of the Lasso model seems to consider all the variables as important, although 'year_song' seems to be much more important than the rest. Here we see for the first time that 'year_song' damages the objective of the study because its objective is to extract the characteristics of the songs to advise the artists or the company, and there is nothing to do about the year.

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Data Dictionary

Predictive Variables

Feature Description
artist_name The artists who performed the track. Each artist object includes a link in href to more detailed information about the artist.
track_name The name of the track.
track_id The Spotify ID for the track.
explicit Whether or not the track has explicit lyrics ( true = yes it does; false = no it does not OR unknown).
acousticness A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
danceability Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
duration_ms_y The duration of the track in milliseconds.
energy Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
instrumentalness Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
key The estimated overall key of the track. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.
liveness Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
loudness The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
mode Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
speechiness Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
tempo The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
time_signature An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure).
valence A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

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Target Variable

Feature Description
popularity The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. The popularity of a track is a value between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Generally speaking, songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past.

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Exploratory Data Analysis

In [55]:
def edaCategorical2 (var="explicit", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table"):
    varDataFrame = pd.DataFrame(df[str(var)])
    classifierDataFrame = pd.DataFrame(dfEncoded[str(classifier)])
    varClassifierDataFrame = pd.concat([varDataFrame, classifierDataFrame], axis=1, sort=False)
    #Contingency table
    data_crosstab = pd.crosstab(varClassifierDataFrame[str(var)],
                            varClassifierDataFrame[str(classifier)],
                               margins = False)
    p = plt.figure()
    #Histogram based on type
    varClassifierDataFrame1 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 1]
    varClassifierDataFrame2 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 2]
    varClassifierDataFrame3 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 3]
    varClassifierDataFrame4 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 4]
    varClassifierDataFrame5 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 5]
    varClassifierDataFrame6 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 6]
    varClassifierDataFrame7 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 7]
    varClassifierDataFrame8 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 8]
    varClassifierDataFrame9 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 9]
    varClassifierDataFrame10 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 10]
    
    # Method 1: on the same Axis
    
    sns.distplot( varClassifierDataFrame1[str(var)] , color="skyblue", label="1")
    sns.distplot( varClassifierDataFrame2[str(var)] , color="red", label="2")
    sns.distplot( varClassifierDataFrame3[str(var)] , color="blue", label="3")
    sns.distplot( varClassifierDataFrame4[str(var)] , color="green", label="4")
    sns.distplot( varClassifierDataFrame5[str(var)] , color="black", label="5")
    sns.distplot( varClassifierDataFrame6[str(var)] , color="gold", label="6")
    sns.distplot( varClassifierDataFrame7[str(var)] , color="grey", label="7")
    sns.distplot( varClassifierDataFrame8[str(var)] , color="purple", label="8")
    sns.distplot( varClassifierDataFrame9[str(var)] , color="yellow", label="9")
    sns.distplot( varClassifierDataFrame10[str(var)] , color="orange", label="10")
    plt.legend()
  
    #Global histogram
    # Cut the window in 2 parts
    f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
    # Add a graph in each part
    sns.boxplot(varClassifierDataFrame[str(var)], ax=ax_box)
    sns.distplot(varClassifierDataFrame[str(var)], ax=ax_hist)
    # Remove x axis name for the boxplot
    ax_box.set(xlabel='')
    plt.close()
    return f
In [56]:
def edaNumeric2 (var, classifier= "popularity", df=dfEncoded, wShowing = "BoxPLot"):
    
    varDataFrame = pd.DataFrame(df[str(var)])
    classifierDataFrame = pd.DataFrame(dfEncoded[str(classifier)])
    varClassifierDataFrame = pd.concat([varDataFrame, classifierDataFrame], axis=1, sort=False)
    b = plt.figure()
    
    #Boxplot
    ax = sns.boxplot(x= str(classifier), y= str(var), data=varClassifierDataFrame)
    # Calculate number of obs per group & median to position labels
    medians = varClassifierDataFrame.groupby([str(classifier)])[str(var)].median().values
    nobs = varClassifierDataFrame[str(var)].value_counts().values
    nobs = [str(x) for x in nobs.tolist()]
    nobs = ["n: " + i for i in nobs]
    # Add it to the plot
    pos = range(len(nobs))
    for tick,label in zip(pos,ax.get_xticklabels()):
        ax.text(pos[tick], medians[tick] + 0.03, nobs[tick],
        horizontalalignment='center', size='x-small', color='w', weight='semibold')
    # plt.close()
    h = plt.figure()
    #Histogram based on type
    varClassifierDataFrame1 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 1]
    varClassifierDataFrame2 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 2]
    varClassifierDataFrame3 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 3]
    varClassifierDataFrame4 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 4]
    varClassifierDataFrame5 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 5]
    varClassifierDataFrame6 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 6]
    varClassifierDataFrame7 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 7]
    varClassifierDataFrame8 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 8]
    varClassifierDataFrame9 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 9]
    varClassifierDataFrame10 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 10]

    # Method 1: on the same Axis
    sns.distplot( varClassifierDataFrame1[str(var)] , color="skyblue", label="1")
    sns.distplot( varClassifierDataFrame2[str(var)] , color="red", label="2")
    sns.distplot( varClassifierDataFrame3[str(var)] , color="blue", label="3")
    sns.distplot( varClassifierDataFrame4[str(var)] , color="green", label="4")
    sns.distplot( varClassifierDataFrame5[str(var)] , color="black", label="5")
    sns.distplot( varClassifierDataFrame6[str(var)] , color="gold", label="6")
    sns.distplot( varClassifierDataFrame7[str(var)] , color="grey", label="7")
    sns.distplot( varClassifierDataFrame8[str(var)] , color="purple", label="8")
    sns.distplot( varClassifierDataFrame9[str(var)] , color="yellow", label="9")
    sns.distplot( varClassifierDataFrame10[str(var)] , color="orange", label="10")
    
    plt.legend()
    # plt.close()
    plt.figure()
    
    #Global histogram
    # Cut the window in 2 parts
    f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
    # Add a graph in each part
    sns.boxplot(varClassifierDataFrame[str(var)], ax=ax_box)
    sns.distplot(varClassifierDataFrame[str(var)], ax=ax_hist)
    # Remove x axis name for the boxplot
    ax_box.set(xlabel='')
    plt.close()
    
    return f

Go to Table of Contents

Numeric variables for the model

In [57]:
edaNumeric2 (var = 'danceability', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[57]:
<Figure size 432x288 with 0 Axes>
In [58]:
edaNumeric2 (var = 'energy', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[58]:
<Figure size 432x288 with 0 Axes>
In [59]:
edaNumeric2 (var = 'loudness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[59]:
<Figure size 432x288 with 0 Axes>
In [60]:
edaNumeric2 (var = 'speechiness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[60]:
<Figure size 432x288 with 0 Axes>
In [61]:
edaNumeric2 (var = 'acousticness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[61]:
<Figure size 432x288 with 0 Axes>
In [62]:
edaNumeric2 (var = 'instrumentalness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[62]:
<Figure size 432x288 with 0 Axes>
In [63]:
edaNumeric2 (var = 'liveness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[63]:
<Figure size 432x288 with 0 Axes>
In [64]:
edaNumeric2 (var = 'valence', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[64]:
<Figure size 432x288 with 0 Axes>
In [65]:
edaNumeric2 (var = 'tempo', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[65]:
<Figure size 432x288 with 0 Axes>
In [66]:
edaNumeric2 (var = 'duration_ms', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[66]:
<Figure size 432x288 with 0 Axes>

Go to Table of Contents

Categorical Variables for the model

In [67]:
 edaCategorical2 (var="explicit", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[67]:
In [68]:
 edaCategorical2 (var="key", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[68]:
In [69]:
 edaCategorical2 (var="mode", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[69]:
In [70]:
 edaCategorical2 (var="time_signature", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[70]:
In [71]:
 edaCategorical2 (var="year_song", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")
    
    

/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide
  return n/db/n.sum(), bin_edges
Out[71]:

Go to Table of Contents

Correlation Matrix

In [72]:
corDf = dfEncoded.drop(['mode', 'explicit'], axis=1)
In [73]:
corDf['popularity'] = pd.to_numeric(corDf['popularity'])
In [74]:
# Compute the correlation matrix
corr = corDf.corr()
In [75]:
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
In [76]:
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(13, 10))

# Generate a custom diverging colormap
# cmap = sns.diverging_palette(10, 220, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask,vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5});

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Modeling

We are going to evaluate each model with the following scores.

In [77]:
scorers = {
    'precision_score': make_scorer(precision_score),
    'recall_score': make_scorer(recall_score),
    'accuracy_score': make_scorer(accuracy_score)
}

Train and Test Data Sets

In [78]:
X_train = predictiveVariablesScaled.drop('year_song', axis = 1)
y_train = popularityTarget

X_test = predictiveVariablesScaledTest.drop('year_song', axis = 1)
y_test = popularityTargetTest
In [79]:
labelsMatrix = []
for i in labels:
    labelsMatrix.append(str(i))

labelsMatrix
Out[79]:
['0', '1', '2', '3', '4', '5', '6']

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Logistic Regression

Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. We can call a Logistic Regression a Linear Regression model but the Logistic Regression uses a more complex cost function, this cost function can be defined as the ‘Sigmoid function’ or also known as the ‘logistic function’ instead of a linear function. The hypothesis of logistic regression tends it to limit the cost function between 0 and 1. Therefore linear functions fail to represent it as it can have a value greater than 1 or less than 0 which is not possible as per the hypothesis of logistic regression.

We expect our classifier to give us a set of outputs or classes based on probability when we pass the inputs through a prediction function and returns a probability score between 0 and 1. We decide with a threshold value above which we classify values into Class 1 and of the value goes below the threshold then we classify it in Class 2. The threshold is set by optimizaing (minimizing) a score (cost function); which in our case are the three scores mentioned at the begining of this report.

Go to Table of Contents

In [80]:
def print_cm(cm, labels, hide_zeroes=False, hide_diagonal=False, hide_threshold=None):
    """pretty print for confusion matrixes"""
    columnwidth = max([len(x) for x in labels] + [5])  # 5 is value length
    empty_cell = " " * columnwidth
    # Print header
    print("    " + empty_cell, end=" ")
    for label in labels:
        print("%{0}s".format(columnwidth) % label, end=" ")
    print()
    # Print rows
    for i, label1 in enumerate(labels):
        print("    %{0}s".format(columnwidth) % label1, end=" ")
        for j in range(len(labels)):
            cell = "%{0}.1f".format(columnwidth) % cm[i, j]
            if hide_zeroes:
                cell = cell if float(cm[i, j]) != 0 else empty_cell
            if hide_diagonal:
                cell = cell if i != j else empty_cell
            if hide_threshold:
                cell = cell if cm[i, j] > hide_threshold else empty_cell
            print(cell, end=" ")
        print()
In [82]:
lr = LogisticRegression(fit_intercept = False, C = 1e9, multi_class = 'multinomial', solver= 'newton-cg' )
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)

Train's Scores

In [83]:
# fit a model
lr = LogisticRegression(fit_intercept = False, C = 1e9);
model = lr.fit(X_train, y_train);
predictionsTest = lr.predict(X_test);
predictionsTrain = lr.predict(X_train);

/Applications/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.
  "this warning.", FutureWarning)
In [84]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)



print("\n")
print(classification_report(y_train, predictionsTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0  25.0 5033.0 2031.0   0.0   0.0   0.0   0.0 
        1  17.0 10052.0 4836.0   0.0   0.0   0.0   0.0 
        2   1.0 7487.0 6550.0   3.0   0.0   0.0   0.0 
        3   0.0 3393.0 3928.0   0.0   0.0   0.0   0.0 
        4   0.0 627.0 1155.0   0.0   0.0   0.0   0.0 
        5   0.0  93.0 206.0   0.0   0.0   0.0   0.0 
        6   0.0   2.0  24.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.58      0.00      0.01      7089
           1       0.38      0.67      0.48     14905
           2       0.35      0.47      0.40     14041
           3       0.00      0.00      0.00      7321
           4       0.00      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       0.00      0.00      0.00        26

    accuracy                           0.37     45463
   macro avg       0.19      0.16      0.13     45463
weighted avg       0.32      0.37      0.28     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Test's Scores

In [85]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)



print("\n")
print(classification_report(y_test, predictionsTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   8.0 1360.0 394.0   0.0   0.0   0.0   0.0 
        1   4.0 2748.0 990.0   0.0   0.0   0.0   0.0 
        2   1.0 2059.0 1367.0   2.0   0.0   0.0   0.0 
        3   1.0 919.0 829.0   0.0   0.0   0.0   0.0 
        4   0.0 179.0 265.0   0.0   0.0   0.0   0.0 
        5   0.0  17.0  39.0   0.0   0.0   0.0   0.0 
        6   0.0   2.0   5.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.57      0.00      0.01      1762
           1       0.38      0.73      0.50      3742
           2       0.35      0.40      0.37      3429
           3       0.00      0.00      0.00      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.37     11189
   macro avg       0.19      0.16      0.13     11189
weighted avg       0.32      0.37      0.28     11189

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Feature Importance

In [86]:
features = X_train.columns


importances = np.abs(np.array(np.std(X_train)) * model.coef_)
indices = np.argsort(importances)
plt.title('Feature Importances')
plt.barh(range(len(indices[0])), importances[0][indices[0]], color='b', align='center')
plt.yticks(range(len(indices[0])), [features[i] for i in indices[0]])
plt.xlabel('Relative Importance')
plt.show()
In [87]:
lista = []
for i in indices[0]:
    lista.append(features[i])

featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
Out[87]:
Feature Importance
0 danceability
1 explicit
2 valence
3 energy
4 loudness
5 speechiness
6 instrumentalness
7 mode
8 duration_ms
9 liveness
10 time_signature
11 acousticness
12 tempo
13 key
In [88]:
features = X_train.columns
#features = iris['feature_names']

importances = np.array(np.std(X_train)) * model.coef_
indices = np.argsort(importances)
plt.title('Feature Importances')
plt.barh(range(len(indices[0])), importances[0][indices[0]], color='b', align='center')
plt.yticks(range(len(indices[0])), [features[i] for i in indices[0]])
plt.xlabel('Relative Importance')
plt.show()

Feature Importance Ditectely Propontional Higher Popularity

In [89]:
lista = []
for i in indices[0]:
    lista.append(features[i])

featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf.head(10)
Out[89]:
Feature Importance
0 valence
1 energy
2 speechiness
3 mode
4 duration_ms
5 liveness
6 key
7 tempo
8 acousticness
9 time_signature

Feature Importance Inversely Proportional to Popularity

In [90]:
featureImportancedf.head(10).iloc[::-1]
Out[90]:
Feature Importance
9 time_signature
8 acousticness
7 tempo
6 key
5 liveness
4 duration_ms
3 mode
2 speechiness
1 energy
0 valence

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Support Vector Machine

The plan is to use different and more complex models each time in order to improve the prediction (i.e. the scores and results).

The next model used is Support Vector Machine. SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.

According to the SVM algorithm we find the points closest to the line from both classes. These points are called support vectors. We compute the distance between the line and the support vectors, this distance is called the margin. Our goal is to maximize the margin. The hyperplane for which the margin is maximum is the optimal hyperplane.

KERNEL

In machine learning, a “kernel” is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem. It entails transforming linearly inseparable data to linearly separable ones. The kernel function is what is applied on each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable.

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Linear Kernel

In [91]:
clf = svm.SVC()
modelSvm = clf.fit(X_train, y_train)
predictionsSvm = clf.predict(X_test)

linear_svc = svm.SVC(kernel='linear')
linear_svc = linear_svc.fit(X_train, y_train)
linear_svc.kernel
predictionsSvmTest = linear_svc.predict(X_test)
predictionsSvmTrain = linear_svc.predict(X_train)

/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)

Train's Scores

In [92]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsSvmTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_train, predictionsSvmTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 6086.0 1003.0   0.0   0.0   0.0   0.0 
        1   0.0 12322.0 2583.0   0.0   0.0   0.0   0.0 
        2   0.0 10470.0 3571.0   0.0   0.0   0.0   0.0 
        3   0.0 5138.0 2183.0   0.0   0.0   0.0   0.0 
        4   0.0 1101.0 681.0   0.0   0.0   0.0   0.0 
        5   0.0 189.0 110.0   0.0   0.0   0.0   0.0 
        6   0.0  14.0  12.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      7089
           1       0.35      0.83      0.49     14905
           2       0.35      0.25      0.30     14041
           3       0.00      0.00      0.00      7321
           4       0.00      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       0.00      0.00      0.00        26

    accuracy                           0.35     45463
   macro avg       0.10      0.15      0.11     45463
weighted avg       0.22      0.35      0.25     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Test's Scores

In [93]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsSvmTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_test, predictionsSvmTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 1513.0 249.0   0.0   0.0   0.0   0.0 
        1   0.0 3157.0 585.0   0.0   0.0   0.0   0.0 
        2   0.0 2575.0 854.0   0.0   0.0   0.0   0.0 
        3   0.0 1218.0 531.0   0.0   0.0   0.0   0.0 
        4   0.0 288.0 156.0   0.0   0.0   0.0   0.0 
        5   0.0  31.0  25.0   0.0   0.0   0.0   0.0 
        6   0.0   5.0   2.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      1762
           1       0.36      0.84      0.50      3742
           2       0.36      0.25      0.29      3429
           3       0.00      0.00      0.00      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.36     11189
   macro avg       0.10      0.16      0.11     11189
weighted avg       0.23      0.36      0.26     11189

Feature Importance

In [94]:
def f_importances(coef, names):
    imp = coef
    imp,names = zip(*sorted(zip(imp,names)))
    plt.barh(range(len(names)), imp, align='center')
    plt.yticks(range(len(names)), names)
    plt.show()

features_names = [X_train.columns]
f_importances(abs(linear_svc.coef_[0]), list(features_names[0]))
In [95]:
lista = []
importances = abs(linear_svc.coef_)
indices = np.argsort(importances)

for i in indices[0]:
    lista.append(features[i])

featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
Out[95]:
Feature Importance
0 loudness
1 explicit
2 time_signature
3 danceability
4 speechiness
5 mode
6 instrumentalness
7 valence
8 key
9 duration_ms
10 acousticness
11 energy
12 tempo
13 liveness

Go to Table of Contents

Polynomial Kernel

In [96]:
linearPoly = svm.SVC(kernel='poly', degree = 4)
linearPoly.kernel
modelSvm = linearPoly.fit(X_train, y_train)
predictionsPolyTest = linearPoly.predict(X_test)
predictionsPolyTrain = linearPoly.predict(X_train)

/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)

Train's Scores

In [97]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsPolyTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_train, predictionsPolyTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   1.0 6218.0 870.0   0.0   0.0   0.0   0.0 
        1   0.0 12461.0 2444.0   0.0   0.0   0.0   0.0 
        2   0.0 10514.0 3527.0   0.0   0.0   0.0   0.0 
        3   0.0 5152.0 2169.0   0.0   0.0   0.0   0.0 
        4   0.0 1105.0 677.0   0.0   0.0   0.0   0.0 
        5   0.0 189.0 110.0   0.0   0.0   0.0   0.0 
        6   0.0  14.0  12.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       1.00      0.00      0.00      7089
           1       0.35      0.84      0.49     14905
           2       0.36      0.25      0.30     14041
           3       0.00      0.00      0.00      7321
           4       0.00      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       0.00      0.00      0.00        26

    accuracy                           0.35     45463
   macro avg       0.24      0.16      0.11     45463
weighted avg       0.38      0.35      0.25     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Test's Scores

In [98]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsPolyTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_test, predictionsPolyTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 1539.0 223.0   0.0   0.0   0.0   0.0 
        1   0.0 3187.0 555.0   0.0   0.0   0.0   0.0 
        2   0.0 2586.0 843.0   0.0   0.0   0.0   0.0 
        3   0.0 1225.0 524.0   0.0   0.0   0.0   0.0 
        4   0.0 288.0 156.0   0.0   0.0   0.0   0.0 
        5   0.0  32.0  24.0   0.0   0.0   0.0   0.0 
        6   0.0   5.0   2.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      1762
           1       0.36      0.85      0.51      3742
           2       0.36      0.25      0.29      3429
           3       0.00      0.00      0.00      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.36     11189
   macro avg       0.10      0.16      0.11     11189
weighted avg       0.23      0.36      0.26     11189

Go to Table of Contents

Radial Basis Function Kernel

In [99]:
linearRbf = svm.SVC(kernel='rbf')
linearRbf.kernel
modelSvm = linearRbf.fit(X_train, y_train)
predictionsRbvTest = linearRbf.predict(X_test)
predictionsRbvTrain = linearRbf.predict(X_train)

/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)

Train's Scores

In [100]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsRbvTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_train, predictionsRbvTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 6125.0 964.0   0.0   0.0   0.0   0.0 
        1   0.0 12260.0 2645.0   0.0   0.0   0.0   0.0 
        2   0.0 10171.0 3870.0   0.0   0.0   0.0   0.0 
        3   0.0 4946.0 2375.0   0.0   0.0   0.0   0.0 
        4   0.0 1075.0 707.0   0.0   0.0   0.0   0.0 
        5   0.0 183.0 116.0   0.0   0.0   0.0   0.0 
        6   0.0  12.0  14.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      7089
           1       0.35      0.82      0.49     14905
           2       0.36      0.28      0.31     14041
           3       0.00      0.00      0.00      7321
           4       0.00      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       0.00      0.00      0.00        26

    accuracy                           0.35     45463
   macro avg       0.10      0.16      0.12     45463
weighted avg       0.23      0.35      0.26     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Test's Scores

In [101]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsRbvTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_test, predictionsRbvTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 1521.0 241.0   0.0   0.0   0.0   0.0 
        1   0.0 3131.0 611.0   0.0   0.0   0.0   0.0 
        2   0.0 2494.0 935.0   0.0   0.0   0.0   0.0 
        3   0.0 1183.0 566.0   0.0   0.0   0.0   0.0 
        4   0.0 274.0 170.0   0.0   0.0   0.0   0.0 
        5   0.0  28.0  28.0   0.0   0.0   0.0   0.0 
        6   0.0   4.0   3.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      1762
           1       0.36      0.84      0.51      3742
           2       0.37      0.27      0.31      3429
           3       0.00      0.00      0.00      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.36     11189
   macro avg       0.10      0.16      0.12     11189
weighted avg       0.23      0.36      0.26     11189

Go to Table of Contents

Sigmoid Kernel

In [102]:
linearSigmoid = svm.SVC(kernel='sigmoid')
linearSigmoid.kernel
modelSvm = linearSigmoid.fit(X_train, y_train)
predictionsSigmoidTest = linearSigmoid.predict(X_test)
predictionsSigmoidTrain = linearSigmoid.predict(X_train)

/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)
Train's Scores
In [103]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsSigmoidTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_train, predictionsSigmoidTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 5328.0 1760.0   1.0   0.0   0.0   0.0 
        1   2.0 10881.0 4020.0   2.0   0.0   0.0   0.0 
        2   1.0 9492.0 4538.0  10.0   0.0   0.0   0.0 
        3   0.0 4814.0 2505.0   2.0   0.0   0.0   0.0 
        4   0.0 1144.0 638.0   0.0   0.0   0.0   0.0 
        5   0.0 173.0 126.0   0.0   0.0   0.0   0.0 
        6   0.0  13.0  13.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      7089
           1       0.34      0.73      0.47     14905
           2       0.33      0.32      0.33     14041
           3       0.13      0.00      0.00      7321
           4       0.00      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       0.00      0.00      0.00        26

    accuracy                           0.34     45463
   macro avg       0.12      0.15      0.11     45463
weighted avg       0.24      0.34      0.25     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
Test's Scores
In [104]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsSigmoidTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_test, predictionsSigmoidTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 1312.0 449.0   1.0   0.0   0.0   0.0 
        1   0.0 2769.0 973.0   0.0   0.0   0.0   0.0 
        2   0.0 2356.0 1071.0   2.0   0.0   0.0   0.0 
        3   0.0 1150.0 598.0   1.0   0.0   0.0   0.0 
        4   0.0 283.0 161.0   0.0   0.0   0.0   0.0 
        5   0.0  43.0  13.0   0.0   0.0   0.0   0.0 
        6   0.0   3.0   4.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      1762
           1       0.35      0.74      0.48      3742
           2       0.33      0.31      0.32      3429
           3       0.25      0.00      0.00      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.34     11189
   macro avg       0.13      0.15      0.11     11189
weighted avg       0.26      0.34      0.26     11189

Go to Table of Contents

Radial Basis Function Kernel

In [105]:
linearRbf = svm.SVC(kernel='rbf')
linearRbf.kernel
modelSvm = linearRbf.fit(X_train, y_train)
predictionsRbvTest = linearRbf.predict(X_test)
predictionsRbvTrain = linearRbf.predict(X_train)

/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)

Train's Scores

In [106]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsRbvTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_train, predictionsRbvTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 6125.0 964.0   0.0   0.0   0.0   0.0 
        1   0.0 12260.0 2645.0   0.0   0.0   0.0   0.0 
        2   0.0 10171.0 3870.0   0.0   0.0   0.0   0.0 
        3   0.0 4946.0 2375.0   0.0   0.0   0.0   0.0 
        4   0.0 1075.0 707.0   0.0   0.0   0.0   0.0 
        5   0.0 183.0 116.0   0.0   0.0   0.0   0.0 
        6   0.0  12.0  14.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      7089
           1       0.35      0.82      0.49     14905
           2       0.36      0.28      0.31     14041
           3       0.00      0.00      0.00      7321
           4       0.00      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       0.00      0.00      0.00        26

    accuracy                           0.35     45463
   macro avg       0.10      0.16      0.12     45463
weighted avg       0.23      0.35      0.26     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Test's Scores

In [107]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsRbvTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_test, predictionsRbvTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0   0.0 1521.0 241.0   0.0   0.0   0.0   0.0 
        1   0.0 3131.0 611.0   0.0   0.0   0.0   0.0 
        2   0.0 2494.0 935.0   0.0   0.0   0.0   0.0 
        3   0.0 1183.0 566.0   0.0   0.0   0.0   0.0 
        4   0.0 274.0 170.0   0.0   0.0   0.0   0.0 
        5   0.0  28.0  28.0   0.0   0.0   0.0   0.0 
        6   0.0   4.0   3.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.00      0.00      0.00      1762
           1       0.36      0.84      0.51      3742
           2       0.37      0.27      0.31      3429
           3       0.00      0.00      0.00      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.36     11189
   macro avg       0.10      0.16      0.12     11189
weighted avg       0.23      0.36      0.26     11189

Go to Table of Contents

Random Forest

Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.

  • Bootstrap Aggregation (or Bagging for short), is a simple and very powerful ensemble method. An ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model.

  • Bootstrap Aggregation is a general procedure that can be used to reduce the variance for those algorithm that have high variance. An algorithm that has high variance are decision trees, like classification and regression trees. Decision trees are sensitive to the specific data on which they are trained. If the training data is changed (e.g. a tree is trained on a subset of the training data) the resulting decision tree can be quite different and in turn the predictions can be quite different.

    • Bagging is the application of the Bootstrap procedure to a high-variance machine learning algorithm, typically decision trees. Bagging uses random samples, independent from each other, where observations have the same probability of being selected.

Go to Table of Contents

In [108]:
randomForestModel = RandomForestClassifier()
randomForestModel.fit(X_train, y_train)
predictionsRandomForestTest = randomForestModel.predict(X_test)
predictionsRandomForestTrain = randomForestModel.predict(X_train)

/Applications/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
  "10 in version 0.20 to 100 in 0.22.", FutureWarning)

Train's Scores

In [109]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsRandomForestTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_train, predictionsRandomForestTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0 7005.0  60.0  23.0   1.0   0.0   0.0   0.0 
        1  34.0 14816.0  39.0  15.0   0.0   1.0   0.0 
        2  24.0 160.0 13842.0  13.0   2.0   0.0   0.0 
        3  19.0  74.0  78.0 7149.0   1.0   0.0   0.0 
        4   2.0  16.0  29.0  11.0 1723.0   1.0   0.0 
        5   0.0   1.0   8.0   1.0   1.0 288.0   0.0 
        6   0.0   1.0   0.0   0.0   0.0   0.0  25.0 


              precision    recall  f1-score   support

           0       0.99      0.99      0.99      7089
           1       0.98      0.99      0.99     14905
           2       0.99      0.99      0.99     14041
           3       0.99      0.98      0.99      7321
           4       1.00      0.97      0.98      1782
           5       0.99      0.96      0.98       299
           6       1.00      0.96      0.98        26

    accuracy                           0.99     45463
   macro avg       0.99      0.98      0.98     45463
weighted avg       0.99      0.99      0.99     45463

Test's Scores

In [110]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsRandomForestTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_test, predictionsRandomForestTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0 320.0 947.0 400.0  93.0   2.0   0.0   0.0 
        1 561.0 1929.0 1033.0 208.0  11.0   0.0   0.0 
        2 444.0 1479.0 1174.0 312.0  19.0   1.0   0.0 
        3 181.0 621.0 696.0 233.0  17.0   1.0   0.0 
        4  51.0 159.0 161.0  68.0   4.0   1.0   0.0 
        5   5.0  16.0  30.0   5.0   0.0   0.0   0.0 
        6   0.0   5.0   1.0   1.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.20      0.18      0.19      1762
           1       0.37      0.52      0.43      3742
           2       0.34      0.34      0.34      3429
           3       0.25      0.13      0.17      1749
           4       0.08      0.01      0.02       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.33     11189
   macro avg       0.18      0.17      0.17     11189
weighted avg       0.30      0.33      0.31     11189


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Go to Table of Contents

Feature Importance

In [111]:
features = X_train.columns
#features = iris['feature_names']

importances = randomForestModel.feature_importances_
indices = np.argsort(importances)
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
In [112]:
lista = []
for i in indices:
    lista.append(features[i])

featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
Out[112]:
Feature Importance
0 duration_ms
1 danceability
2 loudness
3 tempo
4 energy
5 valence
6 acousticness
7 liveness
8 speechiness
9 instrumentalness
10 key
11 mode
12 time_signature
13 explicit

Go to Table of Contents

XGBoost

XGBoost stands for “Extreme Gradient Boosting”.

XGBoost is used for supervised learning problems, where we use the training data (with multiple features) x's to predict a target variable y.

  • Boost: not all data have the same probability of being selected. The cases in which it is wrong assigns them a greater probability of being selected in the following sample to try to correct the error. This presents an overfit problem. The samples are dependent (sequential). When composing uses a weighted average.
In [117]:
# fit model no training data
model = XGBClassifier(objective = 'multi:softmax')
model.fit(X_train, y_train)
Out[117]:
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, gamma=0,
              learning_rate=0.1, max_delta_step=0, max_depth=3,
              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
              nthread=None, objective='multi:softprob', random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
              silent=None, subsample=1, verbosity=1)
In [118]:
# make predictions for test data
y_predTest = model.predict(X_test)
predictionsTest = [round(value) for value in y_predTest]

# make predictions for test data
y_predTrain = model.predict(X_train)
predictionsTrain = [round(value) for value in y_predTrain]

Train's Scores

In [119]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_train, predictionsTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0 121.0 5050.0 1868.0  50.0   0.0   0.0   0.0 
        1  30.0 10526.0 4219.0 130.0   0.0   0.0   0.0 
        2   8.0 6983.0 6803.0 246.0   1.0   0.0   0.0 
        3   1.0 2647.0 4206.0 467.0   0.0   0.0   0.0 
        4   0.0 447.0 1326.0   8.0   1.0   0.0   0.0 
        5   0.0  48.0 251.0   0.0   0.0   0.0   0.0 
        6   0.0   2.0  23.0   0.0   0.0   0.0   1.0 


              precision    recall  f1-score   support

           0       0.76      0.02      0.03      7089
           1       0.41      0.71      0.52     14905
           2       0.36      0.48      0.42     14041
           3       0.52      0.06      0.11      7321
           4       0.50      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       1.00      0.04      0.07        26

    accuracy                           0.39     45463
   macro avg       0.51      0.19      0.17     45463
weighted avg       0.47      0.39      0.32     45463

Test's Scores

In [120]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(y_test, predictionsTest)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0  21.0 1356.0 372.0  13.0   0.0   0.0   0.0 
        1  13.0 2787.0 920.0  22.0   0.0   0.0   0.0 
        2   1.0 1977.0 1419.0  32.0   0.0   0.0   0.0 
        3   2.0 784.0 881.0  81.0   1.0   0.0   0.0 
        4   0.0 150.0 292.0   2.0   0.0   0.0   0.0 
        5   0.0  13.0  43.0   0.0   0.0   0.0   0.0 
        6   0.0   3.0   4.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.57      0.01      0.02      1762
           1       0.39      0.74      0.52      3742
           2       0.36      0.41      0.39      3429
           3       0.54      0.05      0.09      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.39     11189
   macro avg       0.27      0.17      0.14     11189
weighted avg       0.42      0.39      0.31     11189

Go to Table of Contents

Feature Importance

In [121]:
xgb.plot_importance(model);
In [122]:
lista = []
for i in indices:
    lista.append(features[i])

featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
Out[122]:
Feature Importance
0 duration_ms
1 danceability
2 loudness
3 tempo
4 energy
5 valence
6 acousticness
7 liveness
8 speechiness
9 instrumentalness
10 key
11 mode
12 time_signature
13 explicit
In [123]:
xgb.plot_tree(model, num_trees=2)
fig = plt.gcf()
fig.set_size_inches(150, 100)
fig.savefig('../images/tree2.png')
#xgb.to_graphviz(model, num_trees=2)

Go to Table of Contents

CatBoost

is a high-performance open source library for gradient boosting on decision trees. CatBoost has the flexibility of giving indices of categorical columns so that it can be encoded as one-hot encoding

CatBoost is used for supervised learning problems, where we use the training data (with multiple features) x's to predict a target variable y with multiple classes.

Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms.

In [124]:
train_data = X_train
eval_data = X_test 
# cat_features = [0]

train_label =  y_train
eval_label =  y_test
# train_dataset = Pool(data=train_data,
#                      label=train_label,
#                      cat_features=cat_features)
train_dataset = Pool(data=train_data,
                     label=train_label)
eval_dataset = Pool(data=eval_data,
                    label=eval_label)
# eval_dataset = Pool(data=eval_data,
#                     label=eval_label,
#                     cat_features=cat_features)
In [125]:
# Initialize CatBoostClassifier
model = CatBoostClassifier(loss_function='MultiClass')
# Fit model
model.fit(train_dataset)
# Get predicted classes
preds_class = model.predict(eval_dataset)
# Get predicted probabilities for each class
preds_proba = model.predict_proba(eval_dataset)
# Get predicted RawFormulaVal
preds_raw = model.predict(eval_dataset, 
                          prediction_type='RawFormulaVal')

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Train's Scores

In [126]:
print("Confusion matrix:\n")
cm =confusion_matrix(train_label, model.predict(train_dataset))
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(train_label, model.predict(train_dataset)))

Confusion matrix:

              0     1     2     3     4     5     6 
        0 412.0 4868.0 1719.0  89.0   1.0   0.0   0.0 
        1  70.0 10932.0 3707.0 196.0   0.0   0.0   0.0 
        2  27.0 5977.0 7698.0 335.0   4.0   0.0   0.0 
        3   9.0 2384.0 3826.0 1099.0   3.0   0.0   0.0 
        4   1.0 419.0 1194.0 139.0  29.0   0.0   0.0 
        5   0.0  45.0 205.0  41.0   7.0   1.0   0.0 
        6   0.0   1.0  23.0   2.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.79      0.06      0.11      7089
           1       0.44      0.73      0.55     14905
           2       0.42      0.55      0.47     14041
           3       0.58      0.15      0.24      7321
           4       0.66      0.02      0.03      1782
           5       1.00      0.00      0.01       299
           6       0.00      0.00      0.00        26

    accuracy                           0.44     45463
   macro avg       0.56      0.22      0.20     45463
weighted avg       0.52      0.44      0.38     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Test's Scores

In [127]:
print("Confusion matrix:\n")
cm =confusion_matrix(eval_label, preds_class)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)


print("\n")
print(classification_report(eval_label, preds_class)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0  39.0 1239.0 460.0  22.0   2.0   0.0   0.0 
        1  23.0 2490.0 1166.0  62.0   1.0   0.0   0.0 
        2   8.0 1683.0 1595.0 139.0   4.0   0.0   0.0 
        3   4.0 620.0 949.0 171.0   5.0   0.0   0.0 
        4   0.0 108.0 300.0  35.0   1.0   0.0   0.0 
        5   0.0   5.0  46.0   5.0   0.0   0.0   0.0 
        6   0.0   1.0   4.0   2.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.53      0.02      0.04      1762
           1       0.41      0.67      0.50      3742
           2       0.35      0.47      0.40      3429
           3       0.39      0.10      0.16      1749
           4       0.08      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.38     11189
   macro avg       0.25      0.18      0.16     11189
weighted avg       0.39      0.38      0.32     11189

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Feature Importance

In [128]:
model.get_feature_importance(prettified= True)
Out[128]:
Feature Id Importances
0 danceability 11.047897
1 duration_ms 11.005506
2 speechiness 9.240048
3 loudness 8.881768
4 energy 8.687099
5 valence 8.268764
6 acousticness 8.190099
7 instrumentalness 8.180610
8 tempo 7.736335
9 liveness 7.405509
10 key 4.735856
11 explicit 3.432434
12 mode 2.325851
13 time_signature 0.862224

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NGBoost

NGBoost stands for “Natural Gradient Boosting”.

Gradient Boosting methods have generally been among the top performers in predictive accuracy over structured or tabular input data. NGBoost enables predictive uncertainty estimation with Gradient Boosting through probabilistic predictions (including real valued outputs). With the use of Natural Gradients, NGBoost overcomes technical challenges that make generic probabilistic prediction hard with gradient boosting.

In [129]:
from ngboost import NGBClassifier
from ngboost.distns import k_categorical
from ngboost.learners import default_tree_learner
from ngboost.scores import MLE

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split


# X, Y = load_boston(True)
# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

ngb = NGBClassifier(Dist=k_categorical(len(labels)))
ngb.fit(X_train, y_train) # Y should have only 3 values: {0,1,2}

/Applications/anaconda3/lib/python3.7/site-packages/ngboost/distns/categorical.py:47: RuntimeWarning: divide by zero encountered in log
  return -np.log(self.probs[Y, range(len(Y))])

[iter 0] loss=1.4766 val_loss=0.0000 scale=0.5000 norm=4.2247

/Applications/anaconda3/lib/python3.7/site-packages/ngboost/distns/categorical.py:47: RuntimeWarning: divide by zero encountered in log
  return -np.log(self.probs[Y, range(len(Y))])

[iter 100] loss=1.4322 val_loss=0.0000 scale=1.0000 norm=7.0090
[iter 200] loss=1.4129 val_loss=0.0000 scale=1.0000 norm=6.8939
[iter 300] loss=1.4044 val_loss=0.0000 scale=1.0000 norm=6.8734
[iter 400] loss=1.3995 val_loss=0.0000 scale=0.5000 norm=3.4414
Out[129]:
NGBClassifier(Base=DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                                         max_features=None, max_leaf_nodes=None,
                                         min_impurity_decrease=0.0,
                                         min_impurity_split=None,
                                         min_samples_leaf=1,
                                         min_samples_split=2,
                                         min_weight_fraction_leaf=0.0,
                                         presort=False, random_state=None,
                                         splitter='best'),
              Dist=<class 'ngboost.distns.categorical.k_categorical.<locals>.Categorical'>,
              Score=<class 'ngboost.scores.MLE'>, learning_rate=0.01,
              minibatch_frac=1.0, n_estimators=500, natural_gradient=True,
              tol=0.0001, verbose=True, verbose_eval=100)
In [130]:
# make predictions for test data
y_predTest = ngb.predict(X_test)
predictionsTest = [round(value) for value in y_predTest]

# make predictions for test data
y_predTrain = ngb.predict(X_train)
predictionsTrain = [round(value) for value in y_predTrain]

Train's Scores

In [131]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)

print("\n")
print(classification_report(y_train, predictionsTrain)) 

Confusion matrix:

              0     1     2     3     4     5     6 
        0  87.0 5182.0 1781.0  36.0   3.0   0.0   0.0 
        1  10.0 10680.0 4106.0 109.0   0.0   0.0   0.0 
        2   0.0 7753.0 6091.0 197.0   0.0   0.0   0.0 
        3   0.0 3209.0 3772.0 340.0   0.0   0.0   0.0 
        4   0.0 632.0 1138.0  12.0   0.0   0.0   0.0 
        5   0.0  88.0 209.0   2.0   0.0   0.0   0.0 
        6   0.0   4.0  21.0   0.0   0.0   0.0   1.0 


              precision    recall  f1-score   support

           0       0.90      0.01      0.02      7089
           1       0.39      0.72      0.50     14905
           2       0.36      0.43      0.39     14041
           3       0.49      0.05      0.08      7321
           4       0.00      0.00      0.00      1782
           5       0.00      0.00      0.00       299
           6       1.00      0.04      0.07        26

    accuracy                           0.38     45463
   macro avg       0.45      0.18      0.15     45463
weighted avg       0.46      0.38      0.30     45463


/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)

Test's Scores

In [132]:
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsTest))

Confusion matrix:

              0     1     2     3     4     5     6 
        0  17.0 1418.0 318.0   9.0   0.0   0.0   0.0 
        1   5.0 2961.0 758.0  18.0   0.0   0.0   0.0 
        2   0.0 2246.0 1156.0  27.0   0.0   0.0   0.0 
        3   0.0 980.0 712.0  57.0   0.0   0.0   0.0 
        4   0.0 225.0 219.0   0.0   0.0   0.0   0.0 
        5   0.0  18.0  37.0   1.0   0.0   0.0   0.0 
        6   0.0   1.0   6.0   0.0   0.0   0.0   0.0 


              precision    recall  f1-score   support

           0       0.77      0.01      0.02      1762
           1       0.38      0.79      0.51      3742
           2       0.36      0.34      0.35      3429
           3       0.51      0.03      0.06      1749
           4       0.00      0.00      0.00       444
           5       0.00      0.00      0.00        56
           6       0.00      0.00      0.00         7

    accuracy                           0.37     11189
   macro avg       0.29      0.17      0.13     11189
weighted avg       0.44      0.37      0.29     11189

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Feature Importance

In [133]:
lista = []
for i in indices:
    lista.append(features[i])

featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
Out[133]:
Feature Importance
0 duration_ms
1 danceability
2 loudness
3 tempo
4 energy
5 valence
6 acousticness
7 liveness
8 speechiness
9 instrumentalness
10 key
11 mode
12 time_signature
13 explicit

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Conclusions

Based on the proposed scores, the best model to predict the test set was the CatBoost Model; NGBoost had the best precision, but in accuracy, recall and f1-score, the best model is CatBoost. Because the classification is not dichotomous but multiclass, there is no graphical representation of the ROC curve due to dimensionality; In addition, it is not so easy to relate prediction metrics (for example, accuracy) to an established benchmark, the best benchmark for comparison is that it has 100% successes in some class and zero in the others (which on average equals a distribution uniform data by class) which in this case corresponds to 10% (due to the ten classes) [16.6% for 6 classes]

Accuracy precision recall f1-score
Logistic Regression 0.36 0.31 0.36 0.27
SVM 0.35 0.22 0.35 0.25
SVM Polynomial 0.37 0.42 0.37 0.29
SVM RBF 0.37 0.41 0.37 0.29
SVM Sigmoid 0.26 0.27 0.26 0.26
Random Forest 0.35 0.34 0.35 0.3
XGBoost 0.37 0.41 0.37 0.29
CatBoost 0.38 0.4 0.38 0.31
NGBoost 0.37 0.43 0.37 0.28

Looking at the feature importance for the models, every model had different features as the main characteristics to decide the classification, but some interesting conclusions arose from this initial approach.

Feature Importance Logistic Regression SVM Random Forest XGBoost CatBoost NGBoost
1 danceability loudness duration_ms duration_ms duration_ms duration_ms
2 instrumentalness valence loudness loudness danceability loudness
3 explicit time_signature danceability danceability loudness danceability
4 energy explicit valence valence energy valence
5 valence acousticness acousticness acousticness speechiness acousticness
6 mode duration_ms speechiness speechiness valence speechiness
7 liveness key tempo tempo liveness tempo
8 acousticness instrumentalness energy energy acousticness energy
9 speechiness danceability liveness liveness instrumentalness liveness
10 duration_ms mode instrumentalness instrumentalness tempo instrumentalness
11 time_signature tempo key key key key
12 tempo speechiness mode mode explicit mode
13 loudness energy time_signature time_signature mode time_signature
14 key liveness explicit explicit time_signature explicit

XGBOOST and Random Forest models selected a group of features as the most important; duration_ms, loudness, danceability, valence, acousticness, among other. Every feature for these models is related somehow to the rest of the features; happening in both models. On the other hand, the SVM Linear Kernel and the Logistic Regression (both linear) selected a group of features as the most important as well; explicit is the main feature diference between the two groups of models. The only variable shared by the groups is danceability. Apparently, there are two approaches for this classification problem: a linear approach and a non-linear; only by looking at the features each model selects.

Therefore, following what the best model says, if you want a song to reach the maximum level of popularity you must take into account the duration of the song; how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity; the energy it produces, how fast, loud and noisy it is; and level of decibels.

References

https://spotipy.readthedocs.io/en/2.7.0/

https://developer.spotify.com/documentation/web-api/

https://developer.spotify.com/documentation/web-api/reference/tracks/get-several-tracks/

https://en.wikipedia.org/wiki/Precision_and_recall

https://stats.stackexchange.com/questions/367155/why-lasso-for-feature-selection

https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148

https://towardsdatascience.com/kernel-function-6f1d2be6091

https://machinelearningmastery.com/develop-first-xgboost-model-python-scikit-learn/

https://help.bitbond.com/article/20-the-10-loan-status-variants-explained

https://xgboost.readthedocs.io/en/latest/parameter.html#parameters-for-tree-booster

https://www.geeksforgeeks.org/svm-hyperparameter-tuning-using-gridsearchcv-ml/

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

https://scikit-learn.org/stable/modules/svm.html

https://xgboost.readthedocs.io/en/latest/

https://xgboost.readthedocs.io/en/latest/parameter.html

https://github.com/stanfordmlgroup/ngboost/tree/master/examples

https://catboost.ai/

https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db

https://stanfordmlgroup.github.io/projects/ngboost/